Starting Dynare (version 6.4).
Calling Dynare with arguments: none
Starting preprocessing of the model file ... 
Found 16 equation(s). 
Evaluating expressions... 
Computing static model derivatives (order 1). 
Normalizing the static model... 
Finding the optimal block decomposition of the static model... 
7 block(s) found: 
  5 recursive block(s) and 2 simultaneous block(s). 
  the largest simultaneous block has 9 equation(s) 
                                 and 9 feedback variable(s). 
Computing dynamic model derivatives (order 2). 
Normalizing the dynamic model... 
Finding the optimal block decomposition of the dynamic model... 
3 block(s) found: 
  1 recursive block(s) and 2 simultaneous block(s). 
  the largest simultaneous block has 9 equation(s) 
                                 and 7 feedback variable(s). 
Preprocessing completed. 
Preprocessing time: 0h00m00s.
Initial value of the log posterior (or likelihood): -26811.9098
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  8.083544e-23.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 97)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',97,0)">line 97</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Gradient norm  5144317.5608
Minimum Hessian eigenvalue -5.6261e-09
Maximum Hessian eigenvalue 516478396080.9308
 
Iteration 1
Correct for low angle: 1.08734e-12
Predicted improvement: 1002396183238015.625000000
lambda =          1; f = 6074958750257255546880.0000000
lambda =    0.33333; f = 674995416585065463808.0000000
lambda =    0.11111; f = 74999490694945603584.0000000
lambda =   0.037037; f = 8333276731640075264.0000000
lambda =   0.012346; f = 925919632767990400.0000000
lambda =  0.0041152; f = 102879957836157008.0000000
lambda =  0.0013717; f = 11431105972826790.0000000
lambda = 0.00045725; f = 1270122734747918.2500000
lambda = 0.00015242; f = 141124697947317.3750000
lambda = 5.0805e-05; f = 15680505224045.5781250
lambda = 1.6935e-05; f = 1742272784095.2031250
lambda =  5.645e-06; f = 193584022756.4412842
lambda = 1.8817e-06; f =  21508737710.0136795
lambda = 6.2723e-07; f =   2389676256.7004538
lambda = 2.0908e-07; f =    265474316.0721752
lambda = 6.9692e-08; f =     29497951.5600735
lambda = 2.3231e-08; f =      3293713.1635940
lambda = 7.7435e-09; f =       387250.3377396
lambda = 2.5812e-09; f =        66016.4225612

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =        86105.5628649
lambda = -2.0908e-07; f =        33399.0476466
lambda = -6.9692e-08; f =        27543.4655987
lambda = -2.3231e-08; f =        26893.0778448
lambda = -7.7435e-09; f =        26820.8900300
lambda = -2.5812e-09; f =        26812.8949921
Norm of dx 7.7943e+10
Predicted improvement: 13232001582944.640625000
lambda =          1; f = 199542635267.9725342
lambda =    0.33333; f =  22171376886.9636116
lambda =    0.11111; f =   2463493205.7263203
lambda =   0.037037; f =    273739657.8063726
lambda =   0.012346; f =     30437476.5544435
lambda =  0.0041152; f =      3405157.0321027
lambda =  0.0013717; f =       401984.6981708
lambda = 0.00045725; f =        68438.1529946
lambda = 0.00015242; f =        31418.6977320
lambda = 5.0805e-05; f =        27318.2052802
lambda = 1.6935e-05; f =        26866.7339274
lambda =  5.645e-06; f =        26817.6719846
lambda = 1.8817e-06; f =        26812.4540138
lambda = 6.2723e-07; f =        26811.9490927
lambda = 2.0908e-07; f =        26811.9111656
lambda = 6.9692e-08; f =        26811.9098319
lambda = 2.3231e-08; f =          167.4165361
lambda = 7.7435e-09; f =         1640.1628891
lambda = 2.5812e-09; f =         5670.5051448
Norm of dx 5.1443e+06
Gradient step!!
Predicted improvement:      146.983786452
lambda =          1; f =          -24.7584662
Norm of dx   0.005085
Done for param e_a =   0.0212; f = -24.7585
Predicted improvement:       26.250631101
lambda =          1; f =          -58.3482476
Norm of dx    0.03479
Done for param e_v =   0.1636; f = -58.3482
Predicted improvement:       60.685327020
lambda =          1; f =         -150.6257742
lambda =     1.9332; f =         -202.1359970
Norm of dx  0.0024699
Done for param e_g =   0.0179; f = -202.1360
Predicted improvement:       28.399908370
lambda =          1; f =         -250.5085285
lambda =     1.9332; f =         -283.7922375
lambda =     3.7372; f =         -327.4192921
Norm of dx  0.0012905
Done for param e_rer =   0.0172; f = -327.4193
Predicted improvement:        0.054567430
lambda =          1; f =         -327.4710062
Norm of dx 0.00029532
Done for param e_yw =   0.0097; f = -327.4710
Predicted improvement:        0.016132058
lambda =          1; f =         -327.5032539
lambda =     1.9332; f =         -327.5333171
lambda =     3.7372; f =         -327.5913539
lambda =     7.2247; f =         -327.7032469
lambda =     13.967; f =         -327.9184272
lambda =         27; f =         -328.3302033
lambda =     52.196; f =         -329.1106162
lambda =      100.9; f =         -330.5615837
lambda =     195.07; f =         -333.1556856
lambda =      377.1; f =         -337.4154264
lambda =        729; f =         -343.0443255
Norm of dx 2.3824e-05
Done for param alp =   0.3076; f = -343.0443
Predicted improvement:        0.055237086
lambda =          1; f =         -343.1545736
lambda =     1.9332; f =         -343.2570485
lambda =     3.7372; f =         -343.4540413
lambda =     7.2247; f =         -343.8307371
lambda =     13.967; f =         -344.5436815
lambda =         27; f =         -345.8658785
lambda =     52.196; f =         -348.2196750
lambda =      100.9; f =         -352.0627889
lambda =     195.07; f =         -357.1557987
Norm of dx 5.6913e-05
Done for param bet =   0.9253; f = -357.1558
Near-singular H problem.
Correct for low angle: 4.68436e-10
Predicted improvement: 11393046100545.136718750
lambda =          1; f = 113930461005262110720.0000000
lambda =    0.33333; f = 12658940111653758976.0000000
lambda =    0.11111; f = 1406548901280867840.0000000
lambda =   0.037037; f = 156283211248780832.0000000
lambda =   0.012346; f = 17364801248330798.0000000
lambda =  0.0041152; f = 1929422360430285.0000000
lambda =  0.0013717; f = 214380262120800.6875000
lambda = 0.00045725; f = 23820029090678.2890625
lambda = 0.00015242; f = 2646669903567.7509766
lambda = 5.0805e-05; f = 294074451152.7883301
lambda = 1.6935e-05; f =  32674960713.1950340
lambda =  5.645e-06; f =   3630574310.9670630
lambda = 1.8817e-06; f =    403420741.0728988
lambda = 6.2723e-07; f =     44848280.4617137
lambda = 2.0908e-07; f =      5006948.7046745
lambda = 6.9692e-08; f =       580151.6501084
lambda = 2.3231e-08; f =        88291.1724138
lambda = 7.7435e-09; f =        33641.9621260
lambda = 2.5812e-09; f =        27570.4789498

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =     44848347.5065893
lambda = -2.0908e-07; f =      5006971.0529239
lambda = -6.9692e-08; f =       580159.0994824
lambda = -2.3231e-08; f =        88293.6554960
lambda = -7.7435e-09; f =        33642.7897776
lambda = -2.5812e-09; f =        27570.7547912
Norm of dx 1.0674e+10
Done for param delt =   0.0975; f = -357.1558
Predicted improvement:        0.007888172
lambda =          1; f =         -357.1715737
lambda =     1.9332; f =         -357.1862923
lambda =     3.7372; f =         -357.2147394
lambda =     7.2247; f =         -357.2697085
lambda =     13.967; f =         -357.3758824
lambda =         27; f =         -357.5807945
lambda =     52.196; f =         -357.9756499
lambda =      100.9; f =         -358.7341956
lambda =     195.07; f =         -360.1826616
lambda =      377.1; f =         -362.9152390
lambda =        729; f =         -367.9413043
lambda =     1409.3; f =         -376.6669745
lambda =     2724.4; f =         -389.5416024
lambda =     5266.8; f =         -395.7962778
Norm of dx 7.7444e-05
Done for param sig =   1.5917; f = -395.7963
Predicted improvement:        0.000019438
lambda =          1; f =         -395.7963166
lambda =     1.9332; f =         -395.7963528
lambda =     3.7372; f =         -395.7964226
lambda =     7.2247; f =         -395.7965570
lambda =     13.967; f =         -395.7968146
lambda =         27; f =         -395.7973046
lambda =     52.196; f =         -395.7982216
lambda =      100.9; f =         -395.7998817
lambda =     195.07; f =         -395.8026701
lambda =      377.1; f =         -395.8064873
Norm of dx 1.2652e-05
Done for param phi1 =   1.5049; f = -395.8065
Predicted improvement:        0.000188515
lambda =          1; f =         -395.8068643
lambda =     1.9332; f =         -395.8072161
lambda =     3.7372; f =         -395.8078963
lambda =     7.2247; f =         -395.8092109
lambda =     13.967; f =         -395.8117520
lambda =         27; f =         -395.8166628
lambda =     52.196; f =         -395.8261503
lambda =      100.9; f =         -395.8444692
lambda =     195.07; f =         -395.8798004
lambda =      377.1; f =         -395.9477932
lambda =        729; f =         -396.0780803
lambda =     1409.3; f =         -396.3256279
lambda =     2724.4; f =         -396.7880052
lambda =     5266.8; f =         -397.6211964
lambda =      10182; f =         -399.0035854
lambda =      19683; f =         -400.8101703
Norm of dx 3.9032e-05
Done for param phi2 =   4.8316; f = -400.8102
Predicted improvement:        0.016576139
lambda =          1; f =         -400.8433063
lambda =     1.9332; f =         -400.8741985
lambda =     3.7372; f =         -400.9338379
lambda =     7.2247; f =         -401.0488293
lambda =     13.967; f =         -401.2700008
lambda =         27; f =         -401.6933666
lambda =     52.196; f =         -402.4962286
lambda =      100.9; f =         -403.9908655
lambda =     195.07; f =         -406.6710419
lambda =      377.1; f =         -411.1062965
lambda =        729; f =         -417.1083153
Norm of dx 7.6626e-05
Done for param hf =   0.4434; f = -417.1083
Predicted improvement: 1980709641470.521972656
lambda =          1; f = 3961419282852977049600.0000000
lambda =    0.33333; f = 440157698075204911104.0000000
lambda =    0.11111; f = 48906410890721583104.0000000
lambda =   0.037037; f = 5434045652350166016.0000000
lambda =   0.012346; f = 603782849536326912.0000000
lambda =  0.0041152; f = 67086983040229240.0000000
lambda =  0.0013717; f = 7454109146179737.0000000
lambda = 0.00045725; f = 828234322753947.8750000
lambda = 0.00015242; f = 92026026936912.3593750
lambda = 5.0805e-05; f = 10225111145111.0820312
lambda = 1.6935e-05; f = 1136122490126.6281738
lambda =  5.645e-06; f = 126235524644.6979828
lambda = 1.8817e-06; f =  14026082763.2642918
lambda = 6.2723e-07; f =   1558440648.6245222
lambda = 2.0908e-07; f =    173171630.3192028
lambda = 6.9692e-08; f =     19261033.8368304
lambda = 2.3231e-08; f =      2162584.2263433
lambda = 7.7435e-09; f =       263665.7506592
lambda = 2.5812e-09; f =        52977.8951880

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =   1558472197.7318780
lambda = -2.0908e-07; f =    173182146.4220282
lambda = -6.9692e-08; f =     19264538.9381456
lambda = -2.3231e-08; f =      2163752.3271551
lambda = -7.7435e-09; f =       264054.8513032
lambda = -2.5812e-09; f =        53107.3291094
Norm of dx  6.294e+10
Done for param rhoa =   0.7000; f = -417.1083
Predicted improvement:        0.000086191
lambda =          1; f =         -417.1084877
lambda =     1.9332; f =         -417.1086486
lambda =     3.7372; f =         -417.1089595
lambda =     7.2247; f =         -417.1095606
lambda =     13.967; f =         -417.1107225
lambda =         27; f =         -417.1129681
lambda =     52.196; f =         -417.1173073
lambda =      100.9; f =         -417.1256879
lambda =     195.07; f =         -417.1418607
lambda =      377.1; f =         -417.1730192
lambda =        729; f =         -417.2328582
lambda =     1409.3; f =         -417.3470712
lambda =     2724.4; f =         -417.5624748
lambda =     5266.8; f =         -417.9593763
lambda =      10182; f =         -418.6579702
lambda =      19683; f =         -419.7782435
lambda =      38051; f =         -421.2300323
Norm of dx 3.7846e-06
Done for param rhov =   0.3549; f = -421.2300
Predicted improvement:        2.200189033
lambda =          1; f =         -425.1483967
lambda =     1.9332; f =         -427.9344747
lambda =     3.7372; f =         -430.9280157
Norm of dx   0.044164
Done for param rhog =   0.6650; f = -430.9280
Predicted improvement:       13.213779026
lambda =          1; f =         -455.2031900
lambda =     1.9332; f =         -473.9749164
lambda =     3.7372; f =         -499.6545053
Norm of dx    0.12941
Done for param rhorer =   0.4836; f = -499.6545
Predicted improvement:        0.003364330
lambda =          1; f =         -499.6563089
lambda =    0.33333; f =         -499.6562013
lambda =    0.64439; f =         -499.6567969
Norm of dx  0.0072459
Done for param rhoyw =   0.5547; f = -499.6568
Sequence of univariate steps!!
Actual dxnorm 1.0331
FVAL          -499.6568
Improvement   27311.5666
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.251109e-22.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.72958 s.
 
Iteration 2
Correct for low angle: 2.90382e-13
Predicted improvement: 562697632826.030273438
lambda =          1; f = 522963134901.9221802
lambda =    0.33333; f =  58106688722.8057938
lambda =    0.11111; f =   6456189699.6418791
lambda =   0.037037; f =    717317770.0548164
lambda =   0.012346; f =     79689468.8740448
lambda =  0.0041152; f =      8849925.0006302
lambda =  0.0013717; f =       981546.2858079
lambda = 0.00045725; f =       108175.7844921
lambda = 0.00015242; f =        11429.7575531
lambda = 5.0805e-05; f =          778.0291040
lambda = 1.6935e-05; f =         -372.8862461
lambda =  5.645e-06; f =         -489.8957184
lambda = 1.8817e-06; f =         -499.3291602
lambda = 6.2723e-07; f =     79970352.2112821
lambda = 2.0908e-07; f =      8872512.8614296
lambda = 6.9692e-08; f =       981184.0707440
lambda = 2.3231e-08; f =       107180.1396931
lambda = 7.7435e-09; f =        11005.3787199
lambda = 2.5812e-09; f =          631.5483020

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =     79972239.7991650
lambda = -2.0908e-07; f =      8872722.2769680
lambda = -6.9692e-08; f =       981207.2279013
lambda = -2.3231e-08; f =       107182.6673127
lambda = -7.7435e-09; f =        11005.6410802
lambda = -2.5812e-09; f =          631.5711486
Norm of dx 1.4263e+10
Predicted improvement: 124522412.936355397
lambda =          1; f =        24791.4003142
lambda =    0.33333; f =         2240.5504725
lambda =    0.11111; f =         -217.7524068
lambda =   0.037037; f =         -475.1148034
lambda =   0.012346; f =         -498.4716284
lambda =  0.0041152; f =         -499.6541888
lambda =  0.0013717; f =         -474.9021322
lambda = 0.00045725; f =         -497.1820353
lambda = 0.00015242; f =         -499.4579522
lambda = 5.0805e-05; f =         -499.6463756
lambda = 1.6935e-05; f =         -499.6564307
lambda =  5.645e-06; f =         -499.6567966
lambda = 1.8817e-06; f =         -643.4843776
Norm of dx      15781
Gradient step!!
Correct for low angle: 0.00282721
Predicted improvement:       57.780145958
lambda =          1; f =         -499.5637988
lambda =    0.33333; f =         -646.0079983
lambda =    0.11111; f =         -646.3044558
lambda =   0.037037; f =         -644.6469976
lambda =   0.012346; f =         -643.8967577
lambda =  0.0041152; f =         -643.6246017
lambda =  0.0013717; f =         -643.5314262
lambda = 0.00045725; f =         -643.5000946
lambda = 0.00015242; f =         -643.4896204
lambda = 5.0805e-05; f =         -643.4861256
lambda = 1.6935e-05; f =         -643.4849603
lambda =  5.645e-06; f =         -643.4845718
lambda = 1.8817e-06; f =         -643.4844423
lambda = 6.2723e-07; f =         -643.4843992
lambda = 2.0908e-07; f =         -643.4843848
lambda = 6.9692e-08; f =         -643.4843800
lambda = 2.3231e-08; f =         -643.4843784
lambda = 7.7435e-09; f =         -643.4843778
lambda = 2.5812e-09; f =         -643.4843777
Norm of dx     1.4645
Predicted improvement:        0.051146968
lambda =          1; f =         -646.3952409
lambda =     1.9332; f =         -646.4599563
lambda =     3.7372; f =         -646.5340973
Norm of dx 0.00052121
Done for param e_a =   0.0452; f = -646.5341
Predicted improvement:        0.019076128
lambda =          1; f =         -646.5525842
Norm of dx   0.002782
Done for param e_v =   0.1592; f = -646.5526
Predicted improvement: 11413517361737.660156250
lambda =          1; f = 22827034723464685551616.0000000
lambda =    0.33333; f = 2536337191493710118912.0000000
lambda =    0.11111; f = 281815243498512875520.0000000
lambda =   0.037037; f = 31312804832905355264.0000000
lambda =   0.012346; f = 3479200536901900800.0000000
lambda =  0.0041152; f = 386577837404349824.0000000
lambda =  0.0013717; f = 42953093035195976.0000000
lambda = 0.00045725; f = 4772565889555322.0000000
lambda = 0.00015242; f = 530285097757773.0625000
lambda = 5.0805e-05; f = 58920566056665.2421875
lambda = 1.6935e-05; f = 6546729441267.2412109
lambda =  5.645e-06; f = 727414341872.1164551
lambda = 1.8817e-06; f =  80823801970.5061340
lambda = 6.2723e-07; f =   8980417547.3877258
lambda = 2.0908e-07; f =    997822244.5799133
lambda = 6.9692e-08; f =    110868199.7437897
lambda = 2.3231e-08; f =     12318079.9206169
lambda = 7.7435e-09; f =      1368176.4746187
lambda = 2.5812e-09; f =       151557.1587371

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =   8873818420.1350441
lambda = -2.0908e-07; f =    962501381.3775201
lambda = -6.9692e-08; f =     99306757.8914942
lambda = -2.3231e-08; f =      8676445.1850206
lambda = -7.7435e-09; f =       366477.4445888
lambda = -2.5812e-09; f =          -34.1131116
Norm of dx 1.5109e+11
Done for param e_g =   0.0364; f = -646.5526
Predicted improvement:        0.002405737
lambda =          1; f =         -646.5549357
Norm of dx 0.00013669
Done for param e_rer =   0.0225; f = -646.5549
Predicted improvement:        0.000017850
lambda =          1; f =         -646.5549536
Norm of dx 4.9842e-06
Done for param e_yw =   0.0097; f = -646.5550
Predicted improvement:       61.990086429
lambda =          1; f =         -692.6938728
Norm of dx    0.07266
Done for param alp =   0.3901; f = -692.6939
Predicted improvement:        2.588238159
lambda =          1; f =         -695.9172357
Norm of dx  0.0054332
Done for param bet =   0.9216; f = -695.9172
Predicted improvement:        1.769934202
lambda =          1; f =         -697.6663351
Norm of dx  0.0018911
Done for param delt =   0.0995; f = -697.6663
Predicted improvement:        3.416876173
lambda =          1; f =         -702.1280434
Norm of dx    0.11663
Done for param sig =   1.6682; f = -702.1280
Predicted improvement:        0.654021936
lambda =          1; f =         -702.8316409
Norm of dx   0.075972
Done for param phi1 =   1.4792; f = -702.8316
Predicted improvement:        2.309758232
lambda =          1; f =         -705.1015507
Norm of dx    0.68316
Done for param phi2 =   4.0283; f = -705.1016
Predicted improvement:        0.001050839
lambda =          1; f =         -705.1026358
Norm of dx  0.0013343
Done for param hf =   0.4439; f = -705.1026
Predicted improvement:        3.538022135
lambda =          1; f =         -708.8388878
Norm of dx    0.25263
Done for param rhoa =   0.4431; f = -708.8389
Predicted improvement:        2.206784243
lambda =          1; f =         -711.3410653
Norm of dx    0.13394
Done for param rhov =   0.2244; f = -711.3411
Predicted improvement:        0.185438725
lambda =          1; f =         -711.5277326
Norm of dx   0.042957
Done for param rhog =   0.6236; f = -711.5277
Predicted improvement:        4.334011643
lambda =          1; f =         -716.3575617
Norm of dx    0.20025
Done for param rhorer =   0.6857; f = -716.3576
Predicted improvement:        0.000912247
lambda =          1; f =         -716.3584735
Norm of dx  0.0031187
Done for param rhoyw =   0.5556; f = -716.3585
Sequence of univariate steps!!
Actual dxnorm 0.88611
FVAL          -716.3585
Improvement   216.7017
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.552099e-24.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.35498 s.
 
Iteration 3
Near-singular H problem.
Correct for low angle: 1.49625e-23
Predicted improvement: 7660048568894.654296875
lambda =          1; f = 6882363707867278336.0000000
lambda =    0.33333; f = 764707078461001728.0000000
lambda =    0.11111; f = 84967453098693728.0000000
lambda =   0.037037; f = 9440828100863402.0000000
lambda =   0.012346; f = 1048980893024288.5000000
lambda =  0.0041152; f = 116553430200621.5625000
lambda =  0.0013717; f = 12950380347103.8222656
lambda = 0.00045725; f = 1438930887154.9680176
lambda = 0.00015242; f = 159881121752.3959961
lambda = 5.0805e-05; f =  17764539349.2429657
lambda = 1.6935e-05; f =   1973827370.1335194
lambda =  5.645e-06; f =    219310283.2386914
lambda = 1.8817e-06; f =     24366095.6968956
lambda = 6.2723e-07; f =      2706348.8966467
lambda = 2.0908e-07; f =       299949.7729374
lambda = 6.9692e-08; f =        32651.7550029
lambda = 2.3231e-08; f =         2978.4789252
lambda = 7.7435e-09; f =         -309.9142742
lambda = 2.5812e-09; f =         -672.5204582

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =     40007849.8322858
lambda = -2.0908e-07; f =      4444497.9447131
lambda = -6.9692e-08; f =       493139.1004591
lambda = -2.3231e-08; f =        54138.8771771
lambda = -7.7435e-09; f =         5372.9649834
lambda = -2.5812e-09; f =          -41.5950285
Norm of dx  1.042e+10
Predicted improvement:  1080785.779216154
lambda =          1; f =       136173.8063944
lambda =    0.33333; f =        14467.3740669
lambda =    0.11111; f =          962.2355396
lambda =   0.037037; f =         -532.5767118
lambda =   0.012346; f =         -696.8193637
lambda =  0.0041152; f =         -714.4589421
lambda =  0.0013717; f =         -716.2156971
lambda = 0.00045725; f =         -716.3486241
lambda = 0.00015242; f =         -716.3581236
lambda = 5.0805e-05; f =         -716.3584385
lambda = 1.6935e-05; f =         -692.5225854
lambda =  5.645e-06; f =         -718.1937221
lambda = 1.8817e-06; f =         -719.0142282
Norm of dx     1470.2
Gradient step!!
Predicted improvement:       23.489262977
lambda =          1; f =         -716.0834105
lambda =    0.33333; f =         -716.3571911
lambda =    0.11111; f =         -712.2091464
lambda =   0.037037; f =         -719.7546277
Norm of dx     2.9761
Predicted improvement:        0.017376896
lambda =          1; f =         -719.7715173
Norm of dx 0.00070007
Done for param e_a =   0.0421; f = -719.7715
Predicted improvement:        0.594218179
lambda =          1; f =         -720.4339932
Norm of dx   0.013062
Done for param e_v =   0.1739; f = -720.4340
Predicted improvement:        1.021282704
lambda =          1; f =         -721.5988773
Norm of dx  0.0038567
Done for param e_g =   0.0416; f = -721.5989
Predicted improvement:        0.151238741
lambda =          1; f =         -721.7629435
Norm of dx  0.0010989
Done for param e_rer =   0.0264; f = -721.7629
Predicted improvement:        0.000822265
lambda =          1; f =         -721.7637610
Norm of dx 3.4078e-05
Done for param e_yw =   0.0097; f = -721.7638
Predicted improvement:        0.117700859
lambda =          1; f =         -721.8798443
Norm of dx  0.0027503
Done for param alp =   0.4034; f = -721.8798
Predicted improvement:        0.284514049
lambda =          1; f =         -722.1539370
Norm of dx  0.0025126
Done for param bet =   0.9173; f = -722.1539
Predicted improvement:        0.620251899
lambda =          1; f =         -722.7741259
Norm of dx   0.001112
Done for param delt =   0.0997; f = -722.7741
Predicted improvement:        2.493119644
lambda =          1; f =         -725.3598975
Norm of dx    0.13597
Done for param sig =   1.8831; f = -725.3599
Predicted improvement:        0.126898463
lambda =          1; f =         -725.4884973
Norm of dx   0.036207
Done for param phi1 =   1.5221; f = -725.4885
Predicted improvement:        0.576643769
lambda =          1; f =         -726.0719676
Norm of dx    0.33717
Done for param phi2 =   4.3037; f = -726.0720
Predicted improvement:        0.216546733
lambda =          1; f =         -726.2981406
Norm of dx   0.018195
Done for param hf =   0.3908; f = -726.2981
Predicted improvement:        0.171218458
lambda =          1; f =         -726.4677266
Norm of dx   0.055558
Done for param rhoa =   0.4012; f = -726.4677
Predicted improvement:        0.156206484
lambda =          1; f =         -726.6268199
Norm of dx   0.041569
Done for param rhov =   0.2044; f = -726.6268
Predicted improvement:        0.014002260
lambda =          1; f =         -726.6408635
Norm of dx    0.01303
Done for param rhog =   0.6150; f = -726.6409
Predicted improvement:        0.008497955
lambda =          1; f =         -726.6493663
Norm of dx   0.010462
Done for param rhorer =   0.6722; f = -726.6494
Predicted improvement:        0.000760546
lambda =          1; f =         -726.6501272
Norm of dx  0.0028434
Done for param rhoyw =   0.5555; f = -726.6501
Sequence of univariate steps!!
Actual dxnorm 0.36
FVAL          -726.6501
Improvement   10.2917
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  5.549348e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.30265 s.
 
Iteration 4
Correct for low angle: 2.00591e-13
Predicted improvement: 571731712674.656860352
lambda =          1; f = 77657048329333294235648.0000000
lambda =    0.33333; f = 8628560925087489851392.0000000
lambda =    0.11111; f = 958728991545057738752.0000000
lambda =   0.037037; f = 106525443461229821952.0000000
lambda =   0.012346; f = 11836160369988876288.0000000
lambda =  0.0041152; f = 1315128925134692864.0000000
lambda =  0.0013717; f = 146125434504719584.0000000
lambda = 0.00045725; f = 16236158848960394.0000000
lambda = 0.00015242; f = 1804017469733099.5000000
lambda = 5.0805e-05; f = 200446325475012.6562500
lambda = 1.6935e-05; f = 22271793924281.4492188
lambda =  5.645e-06; f = 2474637096478.1835938
lambda = 1.8817e-06; f = 274957452666.7655640
lambda = 6.2723e-07; f =  30550086076.6156616
lambda = 2.0908e-07; f =   3394206251.7349133
lambda = 6.9692e-08; f =    377051017.9971499
lambda = 2.3231e-08; f =     41866462.9912856
lambda = 7.7435e-09; f =      4642039.7782314
lambda = 2.5812e-09; f =       512094.4615356

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =       526442.3555056
lambda = -2.0908e-07; f =        57821.5374004
lambda = -6.9692e-08; f =         5770.2542487
lambda = -2.3231e-08; f =           -7.3985216
lambda = -7.7435e-09; f =         -647.5590432
lambda = -2.5812e-09; f =         -718.1174463
Norm of dx 2.7867e+11
Predicted improvement:   336741.803671313
lambda =          1; f =       207659.1775327
lambda =    0.33333; f =        22395.8103585
lambda =    0.11111; f =         1832.2242549
lambda =   0.037037; f =         -445.7240357
lambda =   0.012346; f =         -696.5431609
lambda =  0.0041152; f =         -723.6498725
lambda =  0.0013717; f =         -726.4077026
lambda = 0.00045725; f =         -726.6325273
lambda = 0.00015242; f =         -726.6490589
lambda = 5.0805e-05; f =         -668.4213644
lambda = 1.6935e-05; f =         -719.5446085
lambda =  5.645e-06; f =         -727.6933855
lambda = 1.8817e-06; f =         -727.5730128
lambda = 3.6376e-06; f =         -727.8827627
Norm of dx     820.66
Gradient step!!
Predicted improvement:       22.581413634
lambda =          1; f =         -726.5116096
lambda =    0.33333; f =         -726.6479144
lambda =    0.11111; f =         -726.6501053
lambda =   0.037037; f =         -726.5692218
lambda =   0.012346; f =         -728.0846733
Norm of dx     6.7478
Predicted improvement:        0.020947135
lambda =          1; f =         -728.1061980
Norm of dx 0.00072195
Done for param e_a =   0.0430; f = -728.1062
Predicted improvement:        0.710871125
lambda =          1; f =         -728.9052577
Norm of dx    0.01552
Done for param e_v =   0.1928; f = -728.9053
Predicted improvement:        0.103546812
lambda =          1; f =         -729.0148398
Norm of dx  0.0015628
Done for param e_g =   0.0441; f = -729.0148
Predicted improvement:        0.003199943
lambda =          1; f =         -729.0180199
Norm of dx 0.00019958
Done for param e_rer =   0.0287; f = -729.0180
Predicted improvement:        0.000045460
lambda =          1; f =         -729.0180653
Norm of dx 7.9599e-06
Done for param e_yw =   0.0097; f = -729.0181
Predicted improvement:        0.016290642
lambda =          1; f =         -729.0343639
Norm of dx  0.0010474
Done for param alp =   0.4048; f = -729.0344
Predicted improvement:        0.371196350
lambda =          1; f =         -729.3877845
Norm of dx  0.0030107
Done for param bet =   0.9168; f = -729.3878
Predicted improvement:        0.177482867
lambda =          1; f =         -729.5652726
Norm of dx 0.00059501
Done for param delt =   0.0997; f = -729.5653
Predicted improvement:        0.747740459
lambda =          1; f =         -730.3206981
Norm of dx   0.083129
Done for param sig =   2.0139; f = -730.3207
Predicted improvement:        0.089277393
lambda =          1; f =         -730.4090411
Norm of dx   0.032119
Done for param phi1 =   1.5046; f = -730.4090
Predicted improvement:        0.154805851
lambda =          1; f =         -730.5647197
Norm of dx    0.18219
Done for param phi2 =   4.5516; f = -730.5647
Predicted improvement:        0.031201623
lambda =          1; f =         -730.5964612
Norm of dx  0.0071813
Done for param hf =   0.3747; f = -730.5965
Predicted improvement:        0.007335253
lambda =          1; f =         -730.6037754
Norm of dx   0.011341
Done for param rhoa =   0.3927; f = -730.6038
Predicted improvement:        0.016147574
lambda =          1; f =         -730.6198992
Norm of dx   0.014105
Done for param rhov =   0.2239; f = -730.6199
Predicted improvement:        0.000650502
lambda =          1; f =         -730.6205501
Norm of dx  0.0029137
Done for param rhog =   0.6141; f = -730.6206
Predicted improvement:        0.040889320
lambda =          1; f =         -730.6614638
Norm of dx   0.024074
Done for param rhorer =   0.6469; f = -730.6615
Predicted improvement:        0.000247548
lambda =          1; f =         -730.6617114
Norm of dx  0.0016225
Done for param rhoyw =   0.5553; f = -730.6617
Sequence of univariate steps!!
Actual dxnorm 0.2838
FVAL          -730.6617
Improvement   4.0116
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  5.235153e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.28248 s.
 
Iteration 5
Correct for low angle: 1.9574e-13
Predicted improvement: 602244188450.813232422
lambda =          1; f = 136441072336420491755520.0000000
lambda =    0.33333; f = 15160119147968921075712.0000000
lambda =    0.11111; f = 1684457682933573746688.0000000
lambda =   0.037037; f = 187161964712369225728.0000000
lambda =   0.012346; f = 20795773837587283968.0000000
lambda =  0.0041152; f = 2310641531062151680.0000000
lambda =  0.0013717; f = 256737945746612640.0000000
lambda = 0.00045725; f = 28526437699895844.0000000
lambda = 0.00015242; f = 3169603950078746.0000000
lambda = 5.0805e-05; f = 352178137075465.8125000
lambda = 1.6935e-05; f = 39130877585752.9218750
lambda =  5.645e-06; f = 4347866442298.3178711
lambda = 1.8817e-06; f = 483093322604.1535645
lambda = 6.2723e-07; f =  53676052497.6589508
lambda = 2.0908e-07; f =   5963677624.1238298
lambda = 6.9692e-08; f =    662521017.3178772
lambda = 2.3231e-08; f =     73576409.4704612
lambda = 7.7435e-09; f =      8162384.0098264
lambda = 2.5812e-09; f =       902247.0587078

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =       729080.5851803
lambda = -2.0908e-07; f =        80329.8137402
lambda = -6.9692e-08; f =         8266.2265529
lambda = -2.3231e-08; f =          265.7400183
lambda = -7.7435e-09; f =         -621.0102295
lambda = -2.5812e-09; f =         -718.8071626
Norm of dx 3.6938e+11
Predicted improvement:   212664.524823083
lambda =          1; f =       193099.8267330
lambda =    0.33333; f =        20775.6008490
lambda =    0.11111; f =         1648.8247178
lambda =   0.037037; f =         -469.6160449
lambda =   0.012346; f =         -702.7461568
lambda =  0.0041152; f =         -727.8987709
lambda =  0.0013717; f =         -730.4432619
lambda = 0.00045725; f =         -730.6460761
lambda = 0.00015242; f =         -730.6607829
lambda = 5.0805e-05; f =         -689.1878031
lambda = 1.6935e-05; f =         -726.5174915
lambda =  5.645e-06; f =         -731.4908689
Norm of dx     652.17
Correct for low angle: 0.00391952
Predicted improvement:       28.675042799
lambda =          1; f =         -730.5004198
lambda =    0.33333; f =         -730.6605337
lambda =    0.11111; f =         -730.6617098
lambda =   0.037037; f =         -725.1076360
lambda =   0.012346; f =         -731.0993227
lambda =  0.0041152; f =         -731.5529501
lambda =  0.0013717; f =         -731.5329671
Norm of dx     17.587
Predicted improvement:        0.016806480
lambda =          1; f =         -731.5701744
Norm of dx  0.0006717
Done for param e_a =   0.0444; f = -731.5702
Predicted improvement:        0.384736527
lambda =          1; f =         -731.9917080
Norm of dx   0.013151
Done for param e_v =   0.2089; f = -731.9917
Predicted improvement:        0.000278735
lambda =          1; f =         -731.9919876
Norm of dx 9.0188e-05
Done for param e_g =   0.0448; f = -731.9920
Predicted improvement:        0.151741982
lambda =          1; f =         -732.1274657
Norm of dx  0.0015974
Done for param e_rer =   0.0297; f = -732.1275
Predicted improvement:        0.081248078
lambda =          1; f =         -732.2104785
Norm of dx   0.002375
Done for param alp =   0.4013; f = -732.2105
Predicted improvement:        0.363032946
lambda =          1; f =         -732.5553885
Norm of dx   0.003059
Done for param bet =   0.9175; f = -732.5554
Predicted improvement:        0.024678487
lambda =          1; f =         -732.5800672
Norm of dx 0.00022189
Done for param delt =   0.0998; f = -732.5801
Predicted improvement:        0.073261707
lambda =          1; f =         -732.6536359
Norm of dx    0.02712
Done for param sig =   2.0750; f = -732.6536
Predicted improvement:        0.219286175
lambda =          1; f =         -732.8693149
Norm of dx   0.051235
Done for param phi1 =   1.4702; f = -732.8693
Predicted improvement:        0.114596603
lambda =          1; f =         -732.9844420
Norm of dx     0.1617
Done for param phi2 =   4.7747; f = -732.9844
Predicted improvement:        0.003010981
lambda =          1; f =         -732.9874363
Norm of dx  0.0022765
Done for param hf =   0.3739; f = -732.9874
Predicted improvement:        0.001573566
lambda =          1; f =         -732.9890074
Norm of dx  0.0052758
Done for param rhoa =   0.3881; f = -732.9890
Predicted improvement:        0.024137060
lambda =          1; f =         -733.0130405
Norm of dx   0.017896
Done for param rhov =   0.2430; f = -733.0130
Predicted improvement:        0.000452802
lambda =          1; f =         -733.0134931
Norm of dx  0.0024568
Done for param rhog =   0.6171; f = -733.0135
Predicted improvement:        0.006846816
lambda =          1; f =         -733.0203417
Norm of dx   0.010066
Done for param rhorer =   0.6365; f = -733.0203
Predicted improvement:        0.000092328
lambda =          1; f =         -733.0204340
Norm of dx 0.00099097
Done for param rhoyw =   0.5552; f = -733.0204
Sequence of univariate steps!!
Actual dxnorm 0.23561
FVAL          -733.0204
Improvement   2.3587
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  5.586192e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.26136 s.
 
Iteration 6
Correct for low angle: 5.16998e-12
Predicted improvement: 20223673355.621765137
lambda =          1; f = 2583251735517028.5000000
lambda =    0.33333; f = 287027966835305.8750000
lambda =    0.11111; f = 31891995055372.5976562
lambda =   0.037037; f = 3543554585836.3779297
lambda =   0.012346; f = 393728146780.0921021
lambda =  0.0041152; f =  43747524588.8116455
lambda =  0.0013717; f =   4860819876.2221003
lambda = 0.00045725; f =    540085270.3038020
lambda = 0.00015242; f =     60007100.0589658
lambda = 5.0805e-05; f =      6666230.1960203
lambda = 1.6935e-05; f =       739849.8238226
lambda =  5.645e-06; f =        81490.7428242
lambda = 1.8817e-06; f =         8382.2107724
lambda = 6.2723e-07; f =          273.1279900
lambda = 2.0908e-07; f =         -623.4046620
lambda = 6.9692e-08; f =         -721.5434724
lambda = 2.3231e-08; f =         -731.9558084
lambda = 7.7435e-09; f =         -732.9487754
lambda = 2.5812e-09; f =         -733.0151513

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =     84160643.7275097
lambda = -2.0908e-07; f =      9337566.0101713
lambda = -6.9692e-08; f =      1032540.0999298
lambda = -2.3231e-08; f =       112642.5705076
lambda = -7.7435e-09; f =        11392.7562405
lambda = -2.5812e-09; f =          463.1152029
Norm of dx 1.4631e+10
Correct for low angle: 0.00319633
Predicted improvement:       32.708512169
lambda =          1; f =         -732.8878119
lambda =    0.33333; f =         -733.0203534
lambda =    0.11111; f =         -732.6379505
lambda =   0.037037; f =         -722.3418768
lambda =   0.012346; f =         -732.3880083
lambda =  0.0041152; f =         -733.1302590
Norm of dx     23.664
Predicted improvement:        0.091886756
lambda =          1; f =         -733.2271135
Norm of dx  0.0015547
Done for param e_a =   0.0463; f = -733.2271
Predicted improvement:        0.311552788
lambda =          1; f =         -733.5661682
Norm of dx   0.012979
Done for param e_v =   0.2254; f = -733.5662
Predicted improvement:        0.042535919
lambda =          1; f =         -733.6103315
Norm of dx  0.0010772
Done for param e_g =   0.0458; f = -733.6103
Predicted improvement:        0.280451081
lambda =          1; f =         -733.9161278
Norm of dx  0.0017428
Done for param e_rer =   0.0317; f = -733.9161
Predicted improvement:        0.319587300
lambda =          1; f =         -734.2408658
Norm of dx  0.0047695
Done for param alp =   0.3959; f = -734.2409
Predicted improvement:        0.297249277
lambda =          1; f =         -734.5236380
Norm of dx  0.0028072
Done for param bet =   0.9202; f = -734.5236
Predicted improvement:        0.019381293
lambda =          1; f =         -734.5430195
Norm of dx 0.00019667
Done for param delt =   0.0998; f = -734.5430
Predicted improvement:        0.005611831
lambda =          1; f =         -734.5486268
Norm of dx  0.0076889
Done for param sig =   2.1110; f = -734.5486
Predicted improvement:        0.215842618
lambda =          1; f =         -734.7609624
Norm of dx   0.051138
Done for param phi1 =   1.4417; f = -734.7610
Predicted improvement:        0.038584515
lambda =          1; f =         -734.7996470
Norm of dx   0.096272
Done for param phi2 =   4.9549; f = -734.7996
Predicted improvement:        0.067368988
lambda =          1; f =         -734.8651760
Norm of dx   0.010968
Done for param hf =   0.3818; f = -734.8652
Predicted improvement:        0.001565285
lambda =          1; f =         -734.8667386
Norm of dx  0.0052755
Done for param rhoa =   0.3834; f = -734.8667
Predicted improvement:        0.022700930
lambda =          1; f =         -734.8892821
Norm of dx   0.017951
Done for param rhov =   0.2616; f = -734.8893
Predicted improvement:        0.000153216
lambda =          1; f =         -734.8894353
Norm of dx  0.0014431
Done for param rhog =   0.6160; f = -734.8894
Predicted improvement:        0.015227904
lambda =          1; f =         -734.9046702
Norm of dx   0.015616
Done for param rhorer =   0.6207; f = -734.9047
Predicted improvement:        0.000116479
lambda =          1; f =         -734.9047867
Norm of dx  0.0011129
Done for param rhoyw =   0.5550; f = -734.9048
Sequence of univariate steps!!
Actual dxnorm 0.18857
FVAL          -734.9048
Improvement   1.8844
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  9.903263e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.43016 s.
 
Iteration 7
Correct for low angle: 1.75058e-13
Predicted improvement: 604837689859.729858398
lambda =          1; f = 4985278127774650368.0000000
lambda =    0.33333; f = 553919791800160128.0000000
lambda =    0.11111; f = 61546643475083696.0000000
lambda =   0.037037; f = 6838515922253059.0000000
lambda =   0.012346; f = 759835095997831.0000000
lambda =  0.0041152; f = 84426119618863.4687500
lambda =  0.0013717; f = 9380679237664.2324219
lambda = 0.00045725; f = 1042297452648.4422607
lambda = 0.00015242; f = 115810747500.5752869
lambda = 5.0805e-05; f =  12867833545.2975140
lambda = 1.6935e-05; f =   1429749755.5683796
lambda =  5.645e-06; f =    158857476.9953471
lambda = 1.8817e-06; f =     17649196.5257854
lambda = 6.2723e-07; f =      1960042.2666177
lambda = 2.0908e-07; f =       217020.8912852
lambda = 6.9692e-08; f =        23424.4794750
lambda = 2.3231e-08; f =         1937.9248742
lambda = 7.7435e-09; f =         -441.5346014
lambda = 2.5812e-09; f =         -703.4809461

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 135565044934.5668488
lambda = -2.0908e-07; f =  15062261592.8398361
lambda = -6.9692e-08; f =   1673410466.2646914
lambda = -2.3231e-08; f =    185876015.2573273
lambda = -7.7435e-09; f =     20632967.4940530
lambda = -2.5812e-09; f =      2285481.4424081
Norm of dx 5.8703e+11
Predicted improvement:       18.601237296
lambda =          1; f =         -734.8459718
lambda =    0.33333; f =         -734.9041029
lambda =    0.11111; f =         -734.9047653
lambda =   0.037037; f =         -727.2540249
lambda =   0.012346; f =         -734.3615316
lambda =  0.0041152; f =         -734.9465004
lambda =  0.0013717; f =         -734.9434432
lambda =  0.0026518; f =         -734.9571906
Norm of dx     11.663
Predicted improvement:        0.129225655
lambda =          1; f =         -735.0945222
Norm of dx  0.0018942
Done for param e_a =   0.0483; f = -735.0945
Predicted improvement:        0.100340248
lambda =          1; f =         -735.2005385
Norm of dx  0.0081881
Done for param e_v =   0.2346; f = -735.2005
Predicted improvement:        0.018759885
lambda =          1; f =         -735.2197920
Norm of dx  0.0007409
Done for param e_g =   0.0465; f = -735.2198
Predicted improvement:        0.151901786
lambda =          1; f =         -735.3818389
Norm of dx  0.0013981
Done for param e_rer =   0.0331; f = -735.3818
Predicted improvement:        0.350010447
lambda =          1; f =         -735.7445962
Norm of dx  0.0049914
Done for param alp =   0.3900; f = -735.7446
Predicted improvement:        0.218509869
lambda =          1; f =         -735.9542459
Norm of dx    0.00241
Done for param bet =   0.9230; f = -735.9542
Predicted improvement:        0.035361750
lambda =          1; f =         -735.9896081
Norm of dx 0.00026567
Done for param delt =   0.0998; f = -735.9896
Predicted improvement:        0.007523948
lambda =          1; f =         -735.9971216
Norm of dx  0.0088883
Done for param sig =   2.1161; f = -735.9971
Predicted improvement:        0.046573528
lambda =          1; f =         -736.0433429
Norm of dx   0.023452
Done for param phi1 =   1.4210; f = -736.0433
Predicted improvement:        0.024013626
lambda =          1; f =         -736.0674069
Norm of dx   0.076979
Done for param phi2 =   5.0592; f = -736.0674
Predicted improvement:        0.154822498
lambda =          1; f =         -736.2155851
Norm of dx   0.016666
Done for param hf =   0.3971; f = -736.2156
Predicted improvement:        0.001574823
lambda =          1; f =         -736.2171549
Norm of dx  0.0053026
Done for param rhoa =   0.3786; f = -736.2172
Predicted improvement:        0.003721446
lambda =          1; f =         -736.2208622
Norm of dx  0.0073519
Done for param rhov =   0.2695; f = -736.2209
Predicted improvement:        0.000034958
lambda =          1; f =         -736.2208972
Norm of dx 0.00069391
Done for param rhog =   0.6155; f = -736.2209
Predicted improvement:        0.007668121
lambda =          1; f =         -736.2285679
Norm of dx   0.011386
Done for param rhorer =   0.6092; f = -736.2286
Predicted improvement:        0.000020187
lambda =          1; f =         -736.2285881
Norm of dx 0.00046333
Done for param rhoyw =   0.5549; f = -736.2286
Sequence of univariate steps!!
Actual dxnorm 0.10917
FVAL          -736.2286
Improvement   1.3238
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.124978e-24.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.28606 s.
 
Iteration 8
Correct for low angle: 2.01537e-13
Predicted improvement: 539332469622.715270996
lambda =          1; f = 6628883576204348416.0000000
lambda =    0.33333; f = 736542619375641856.0000000
lambda =    0.11111; f = 81838068751975840.0000000
lambda =   0.037037; f = 9093118727705786.0000000
lambda =   0.012346; f = 1010346517795628.7500000
lambda =  0.0041152; f = 112260721697412.9375000
lambda =  0.0013717; f = 12473412687470.8339844
lambda = 0.00045725; f = 1385934464467.4980469
lambda = 0.00015242; f = 153992624982.0259399
lambda = 5.0805e-05; f =  17110260135.6584759
lambda = 1.6935e-05; f =   1901129073.6988249
lambda =  5.645e-06; f =    211232484.9155504
lambda = 1.8817e-06; f =     23468484.7874954
lambda = 6.2723e-07; f =      2606577.4848532
lambda = 2.0908e-07; f =       288839.9081546
lambda = 6.9692e-08; f =        31397.5519419
lambda = 2.3231e-08; f =         2820.8181003
lambda = 7.7435e-09; f =         -345.1578247
lambda = 2.5812e-09; f =         -694.1175273

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f = 164468912651.2007446
lambda = -2.0908e-07; f =  18273749637.9495201
lambda = -6.9692e-08; f =   2030224866.3824563
lambda = -2.3231e-08; f =    225516190.6212166
lambda = -7.7435e-09; f =     25035474.3802655
lambda = -2.5812e-09; f =      2773995.7561552
Norm of dx 6.4658e+11
Correct for low angle: 0.00448757
Predicted improvement:       20.334601389
lambda =          1; f =         -736.1083559
lambda =    0.33333; f =         -736.2276735
lambda =    0.11111; f =         -736.2285523
lambda =   0.037037; f =         -717.7497922
lambda =   0.012346; f =         -734.5155013
lambda =  0.0041152; f =         -736.1499777
lambda =  0.0013717; f =         -736.2570503
Norm of dx     24.379
Predicted improvement:        0.146732916
lambda =          1; f =         -736.4135413
Norm of dx  0.0020967
Done for param e_a =   0.0505; f = -736.4135
Predicted improvement:        0.022795988
lambda =          1; f =         -736.4369894
Norm of dx  0.0041898
Done for param e_v =   0.2401; f = -736.4370
Predicted improvement:        0.007671592
lambda =          1; f =         -736.4447908
Norm of dx 0.00048563
Done for param e_g =   0.0469; f = -736.4448
Predicted improvement:        0.067306932
lambda =          1; f =         -736.5152713
Norm of dx  0.0009958
Done for param e_rer =   0.0342; f = -736.5153
Predicted improvement:        0.345670101
lambda =          1; f =         -736.8745379
Norm of dx   0.004992
Done for param alp =   0.3843; f = -736.8745
Predicted improvement:        0.161901606
lambda =          1; f =         -737.0310334
Norm of dx  0.0020799
Done for param bet =   0.9253; f = -737.0310
Predicted improvement:        0.012344362
lambda =          1; f =         -737.0433778
Norm of dx 0.00015698
Done for param delt =   0.0998; f = -737.0434
Predicted improvement:        0.010259868
lambda =          1; f =         -737.0536200
Norm of dx   0.010299
Done for param sig =   2.1201; f = -737.0536
Predicted improvement:        0.021611856
lambda =          1; f =         -737.0751151
Norm of dx    0.01583
Done for param phi1 =   1.4135; f = -737.0751
Predicted improvement:        0.009564988
lambda =          1; f =         -737.0846921
Norm of dx   0.049095
Done for param phi2 =   5.1373; f = -737.0847
Predicted improvement:        0.178459135
lambda =          1; f =         -737.2548002
Norm of dx   0.017579
Done for param hf =   0.4139; f = -737.2548
Predicted improvement:        0.001590539
lambda =          1; f =         -737.2563876
Norm of dx   0.005344
Done for param rhoa =   0.3735; f = -737.2564
Predicted improvement:        0.000012058
lambda =          1; f =         -737.2563996
Norm of dx 0.00040919
Done for param rhog =   0.6161; f = -737.2564
Predicted improvement:        0.003791697
lambda =          1; f =         -737.2601923
Norm of dx  0.0081506
Done for param rhorer =   0.6010; f = -737.2602
Predicted improvement:        0.000010386
lambda =          1; f =         -737.2602026
Norm of dx 0.00033232
Done for param rhoyw =   0.5549; f = -737.2602
Sequence of univariate steps!!
Actual dxnorm 0.081391
FVAL          -737.2602
Improvement   1.0316
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  4.292990e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.31906 s.
 
Iteration 9
Correct for low angle: 2.2481e-12
Predicted improvement: 38428481413.793922424
lambda =          1; f = 49372814878343552.0000000
lambda =    0.33333; f = 5485868302367808.0000000
lambda =    0.11111; f = 609540916668834.2500000
lambda =   0.037037; f = 67726766579502.9218750
lambda =   0.012346; f = 7525195639762.0830078
lambda =  0.0041152; f = 836132632815.3955078
lambda =  0.0013717; f =  92903553420.8497925
lambda = 0.00045725; f =  10322592465.5284634
lambda = 0.00015242; f =   1146946092.5910470
lambda = 5.0805e-05; f =    127435147.4085422
lambda = 1.6935e-05; f =     14157926.0762398
lambda =  5.645e-06; f =      1572155.4552423
lambda = 1.8817e-06; f =       173931.6908569
lambda = 6.2723e-07; f =        18638.5593151
lambda = 2.0908e-07; f =         1405.4006493
lambda = 6.9692e-08; f =         -502.3287519
lambda = 2.3231e-08; f =         -712.1720486
lambda = 7.7435e-09; f =         -734.7911197
lambda = 2.5812e-09; f =         -737.0720715

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =   1166802619.0964212
lambda = -2.0908e-07; f =    129595793.8382964
lambda = -6.9692e-08; f =     14382788.0505597
lambda = -2.3231e-08; f =      1592075.1447439
lambda = -7.7435e-09; f =       174462.2029493
lambda = -2.5812e-09; f =        18142.1142189
Norm of dx 5.4465e+10
Correct for low angle: 0.00495417
Predicted improvement:       17.397304406
lambda =          1; f =         -737.1071179
lambda =    0.33333; f =         -737.2591357
lambda =    0.11111; f =         -737.2601566
lambda =   0.037037; f =         -717.2011206
lambda =   0.012346; f =         -735.3200104
lambda =  0.0041152; f =         -737.1401046
lambda =  0.0013717; f =         -737.2786778
Norm of dx     24.658
Predicted improvement:        0.141142002
lambda =          1; f =         -737.4290453
Norm of dx  0.0021535
Done for param e_a =   0.0528; f = -737.4290
Predicted improvement:        0.001677184
lambda =          1; f =         -737.4307361
Norm of dx  0.0011861
Done for param e_v =   0.2425; f = -737.4307
Predicted improvement:        0.003279089
lambda =          1; f =         -737.4340522
Norm of dx 0.00032239
Done for param e_g =   0.0472; f = -737.4341
Predicted improvement:        0.033219139
lambda =          1; f =         -737.4684100
Norm of dx 0.00073147
Done for param e_rer =   0.0349; f = -737.4684
Predicted improvement:        0.327169739
lambda =          1; f =         -737.8044138
Norm of dx  0.0049201
Done for param alp =   0.3787; f = -737.8044
Predicted improvement:        0.121377778
lambda =          1; f =         -737.9221788
Norm of dx   0.001808
Done for param bet =   0.9275; f = -737.9222
Predicted improvement:        0.013875299
lambda =          1; f =         -737.9360542
Norm of dx 0.00016643
Done for param delt =   0.0998; f = -737.9361
Predicted improvement:        0.013724029
lambda =          1; f =         -737.9497516
Norm of dx    0.01179
Done for param sig =   2.1226; f = -737.9498
Predicted improvement:        0.014247964
lambda =          1; f =         -737.9639330
Norm of dx   0.012741
Done for param phi1 =   1.4093; f = -737.9639
Predicted improvement:        0.003341368
lambda =          1; f =         -737.9672768
Norm of dx   0.029237
Done for param phi2 =   5.1960; f = -737.9673
Predicted improvement:        0.181363580
lambda =          1; f =         -738.1400099
Norm of dx    0.01731
Done for param hf =   0.4305; f = -738.1400
Predicted improvement:        0.001490633
lambda =          1; f =         -738.1414976
Norm of dx  0.0051867
Done for param rhoa =   0.3686; f = -738.1415
Predicted improvement:        0.001721163
lambda =          1; f =         -738.1432235
Norm of dx  0.0050251
Done for param rhov =   0.2645; f = -738.1432
Predicted improvement:        0.000097520
lambda =          1; f =         -738.1433210
Norm of dx   0.001166
Done for param rhog =   0.6174; f = -738.1433
Predicted improvement:        0.001934539
lambda =          1; f =         -738.1452559
Norm of dx  0.0058953
Done for param rhorer =   0.5950; f = -738.1453
Sequence of univariate steps!!
Actual dxnorm 0.062242
FVAL          -738.1453
Improvement   0.88505
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.691475e-24.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.32549 s.
 
Iteration 10
Correct for low angle: 9.61643e-13
Predicted improvement: 83830658719.523132324
lambda =          1; f = 19028643376888281038848.0000000
lambda =    0.33333; f = 2114293708348096512000.0000000
lambda =    0.11111; f = 234921523084773163008.0000000
lambda =   0.037037; f = 26102391432191918080.0000000
lambda =   0.012346; f = 2900265707464067072.0000000
lambda =  0.0041152; f = 322251742865810240.0000000
lambda =  0.0013717; f = 35805748404653500.0000000
lambda = 0.00045725; f = 3978416221852606.5000000
lambda = 0.00015242; f = 442046157687639.0625000
lambda = 5.0805e-05; f = 49116210014353.9765625
lambda = 1.6935e-05; f = 5457346758263.1308594
lambda =  5.645e-06; f = 606368558269.7198486
lambda = 1.8817e-06; f =  67373182568.6364517
lambda = 6.2723e-07; f =   7485541515.5295753
lambda = 2.0908e-07; f =    831603851.2176102
lambda = 6.9692e-08; f =     92359002.3008831
lambda = 2.3231e-08; f =     10247871.6706686
lambda = 7.7435e-09; f =      1133474.4468850
lambda = 2.5812e-09; f =       123784.2126919

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =        57571.7618260
lambda = -2.0908e-07; f =         5731.7249740
lambda = -6.9692e-08; f =          -22.0937469
lambda = -2.3231e-08; f =         -659.3832850
lambda = -7.7435e-09; f =         -729.6473839
lambda = -2.5812e-09; f =         -737.2820000
Norm of dx 1.3794e+11
Predicted improvement:       15.451696337
lambda =          1; f =         -738.0021029
lambda =    0.33333; f =         -738.1430803
lambda =    0.11111; f =         -738.1451250
lambda =   0.037037; f =         -721.1639193
lambda =   0.012346; f =         -736.5129063
lambda =  0.0041152; f =         -738.0486702
lambda =  0.0013717; f =         -738.1627852
Norm of dx     4.6106
Predicted improvement:        0.126590269
lambda =          1; f =         -738.2972817
Norm of dx  0.0021367
Done for param e_a =   0.0550; f = -738.2973
Predicted improvement:        0.000570463
lambda =          1; f =         -738.2978494
Norm of dx 0.00070382
Done for param e_v =   0.2419; f = -738.2978
Predicted improvement:        0.001245924
lambda =          1; f =         -738.2991041
Norm of dx 0.00020078
Done for param e_g =   0.0474; f = -738.2991
Predicted improvement:        0.016279341
lambda =          1; f =         -738.3157805
Norm of dx 0.00052875
Done for param e_rer =   0.0355; f = -738.3158
Predicted improvement:        0.286151675
lambda =          1; f =         -738.6104910
Norm of dx  0.0046165
Done for param alp =   0.3733; f = -738.6105
Predicted improvement:        0.083796946
lambda =          1; f =         -738.6922131
Norm of dx  0.0015038
Done for param bet =   0.9293; f = -738.6922
Predicted improvement:        0.025016780
lambda =          1; f =         -738.7172301
Norm of dx 0.00022349
Done for param delt =   0.0998; f = -738.7172
Predicted improvement:        0.005022468
lambda =          1; f =         -738.7222463
Norm of dx  0.0070283
Done for param sig =   2.1185; f = -738.7222
Predicted improvement:        0.001086801
lambda =          1; f =         -738.7233317
Norm of dx  0.0034739
Done for param phi1 =   1.4049; f = -738.7233
Predicted improvement:        0.003923543
lambda =          1; f =         -738.7272584
Norm of dx   0.031805
Done for param phi2 =   5.2332; f = -738.7273
Predicted improvement:        0.178719887
lambda =          1; f =         -738.8974785
Norm of dx   0.016761
Done for param hf =   0.4467; f = -738.8975
Predicted improvement:        0.001250114
lambda =          1; f =         -738.8987262
Norm of dx  0.0047575
Done for param rhoa =   0.3642; f = -738.8987
Predicted improvement:        0.007492248
lambda =          1; f =         -738.9062594
Norm of dx   0.010431
Done for param rhov =   0.2544; f = -738.9063
Predicted improvement:        0.000205815
lambda =          1; f =         -738.9064652
Norm of dx  0.0016953
Done for param rhog =   0.6192; f = -738.9065
Predicted improvement:        0.000855310
lambda =          1; f =         -738.9073206
Norm of dx  0.0039564
Done for param rhorer =   0.5910; f = -738.9073
Sequence of univariate steps!!
Actual dxnorm 0.043179
FVAL          -738.9073
Improvement   0.76206
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  8.997882e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.33004 s.
 
Iteration 11
Near-singular H problem.
Correct for low angle: 1.19978e-22
Predicted improvement: 832725792917.211059570
lambda =          1; f = 43896949246150148096.0000000
lambda =    0.33333; f = 4877438804612939776.0000000
lambda =    0.11111; f = 541937644785375040.0000000
lambda =   0.037037; f = 60215293807835128.0000000
lambda =   0.012346; f = 6690588181801282.0000000
lambda =  0.0041152; f = 743398680509949.6250000
lambda =  0.0013717; f = 82599851270610.3437500
lambda = 0.00045725; f = 9177760545397.6660156
lambda = 0.00015242; f = 1019750935647.8366699
lambda = 5.0805e-05; f = 113305580396.3437805
lambda = 1.6935e-05; f =  12589482127.1556206
lambda =  5.645e-06; f =   1398821979.7939575
lambda = 1.8817e-06; f =    155421109.4550015
lambda = 6.2723e-07; f =     17267394.6778152
lambda = 2.0908e-07; f =      1917623.9783110
lambda = 6.9692e-08; f =       212306.9135940
lambda = 2.3231e-08; f =        22898.1507266
lambda = 7.7435e-09; f =         1876.2710810
lambda = 2.5812e-09; f =         -451.7060684

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =      7181654.9549488
lambda = -2.0908e-07; f =       797197.6895412
lambda = -6.9692e-08; f =        87885.1698611
lambda = -2.3231e-08; f =         9096.5413147
lambda = -7.7435e-09; f =          350.2071341
lambda = -2.5812e-09; f =         -618.9557227
Norm of dx 7.8838e+09
Predicted improvement:       14.612603962
lambda =          1; f =         -738.7107939
lambda =    0.33333; f =         -738.9046546
lambda =    0.11111; f =         -738.9072038
lambda =   0.037037; f =         -719.5434747
lambda =   0.012346; f =         -736.9965155
lambda =  0.0041152; f =         -738.7751866
lambda =  0.0013717; f =         -738.9193651
Norm of dx     14.745
Predicted improvement:        0.110186783
lambda =          1; f =         -739.0360012
Norm of dx  0.0020873
Done for param e_a =   0.0572; f = -739.0360
Predicted improvement:        0.001991434
lambda =          1; f =         -739.0379744
Norm of dx  0.0013201
Done for param e_v =   0.2413; f = -739.0380
Predicted improvement:        0.000367115
lambda =          1; f =         -739.0383429
Norm of dx 0.00010972
Done for param e_g =   0.0475; f = -739.0383
Predicted improvement:        0.007054379
lambda =          1; f =         -739.0455132
Norm of dx 0.00035646
Done for param e_rer =   0.0359; f = -739.0455
Predicted improvement:        0.239485350
lambda =          1; f =         -739.2910039
Norm of dx  0.0042553
Done for param alp =   0.3683; f = -739.2910
Predicted improvement:        0.057015505
lambda =          1; f =         -739.3467782
Norm of dx  0.0012444
Done for param bet =   0.9309; f = -739.3468
Predicted improvement:        0.023329376
lambda =          1; f =         -739.3701078
Norm of dx 0.00021583
Done for param delt =   0.0998; f = -739.3701
Predicted improvement:        0.006834346
lambda =          1; f =         -739.3769313
Norm of dx  0.0080986
Done for param sig =   2.1188; f = -739.3769
Predicted improvement:        0.001963422
lambda =          1; f =         -739.3788913
Norm of dx   0.004632
Done for param phi1 =   1.4046; f = -739.3789
Predicted improvement:        0.002247451
lambda =          1; f =         -739.3811401
Norm of dx   0.024189
Done for param phi2 =   5.2752; f = -739.3811
Predicted improvement:        0.156816625
lambda =          1; f =         -739.5309688
Norm of dx   0.015262
Done for param hf =   0.4615; f = -739.5310
Predicted improvement:        0.001079016
lambda =          1; f =         -739.5320459
Norm of dx  0.0044276
Done for param rhoa =   0.3602; f = -739.5320
Predicted improvement:        0.008675953
lambda =          1; f =         -739.5407648
Norm of dx   0.011197
Done for param rhov =   0.2433; f = -739.5408
Predicted improvement:        0.000306627
lambda =          1; f =         -739.5410713
Norm of dx  0.0020687
Done for param rhog =   0.6214; f = -739.5411
Predicted improvement:        0.000428647
lambda =          1; f =         -739.5415000
Norm of dx  0.0028177
Done for param rhorer =   0.5880; f = -739.5415
Sequence of univariate steps!!
Actual dxnorm 0.046591
FVAL          -739.5415
Improvement   0.63418
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.305957e-24.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.31105 s.
 
Iteration 12
Near-singular H problem.
Correct for low angle: 1.69561e-22
Predicted improvement: 693446272080.106811523
lambda =          1; f = 40999268480664576000.0000000
lambda =    0.33333; f = 4555474275133999616.0000000
lambda =    0.11111; f = 506163808183089216.0000000
lambda =   0.037037; f = 56240423076409664.0000000
lambda =   0.012346; f = 6248935879030178.0000000
lambda =  0.0041152; f = 694326202664475.8750000
lambda =  0.0013717; f = 77147353812292.2968750
lambda = 0.00045725; f = 8571927521161.8134766
lambda = 0.00015242; f = 952436164072.9012451
lambda = 5.0805e-05; f = 105826164297.4680176
lambda = 1.6935e-05; f =  11758436882.0500183
lambda =  5.645e-06; f =   1306483948.3617661
lambda = 1.8817e-06; f =    145161437.6639524
lambda = 6.2723e-07; f =     16127467.3691911
lambda = 2.0908e-07; f =      1790977.0623199
lambda = 6.9692e-08; f =       198238.5861635
lambda = 2.3231e-08; f =        21335.7563364
lambda = 7.7435e-09; f =         1702.5308629
lambda = 2.5812e-09; f =         -471.4202575

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =      6175627.5369935
lambda = -2.0908e-07; f =       685421.0265136
lambda = -6.9692e-08; f =        75466.5518530
lambda = -2.3231e-08; f =         7716.6560957
lambda = -7.7435e-09; f =          196.4977836
lambda = -2.5812e-09; f =         -636.5394519
Norm of dx 7.5299e+09
Predicted improvement:       12.878779830
lambda =          1; f =         -739.3174108
lambda =    0.33333; f =         -739.5378739
lambda =    0.11111; f =         -739.5413779
lambda =   0.037037; f =         -721.8993838
lambda =   0.012346; f =         -737.7933719
lambda =  0.0041152; f =         -739.4179312
lambda =  0.0013717; f =         -739.5513253
lambda = 0.00045725; f =         -739.5504434
lambda = 0.00088394; f =         -739.5536764
Norm of dx     9.5451
Predicted improvement:        0.091116882
lambda =          1; f =         -739.6496699
Norm of dx  0.0019835
Done for param e_a =   0.0592; f = -739.6497
Predicted improvement:        0.003395435
lambda =          1; f =         -739.6530245
Norm of dx  0.0017206
Done for param e_v =   0.2398; f = -739.6530
Predicted improvement:        0.000055295
lambda =          1; f =         -739.6530798
Norm of dx 4.2758e-05
Done for param e_g =   0.0475; f = -739.6531
Predicted improvement:        0.003580765
lambda =          1; f =         -739.6567030
Norm of dx 0.00025777
Done for param e_rer =   0.0362; f = -739.6567
Predicted improvement:        0.193834460
lambda =          1; f =         -739.8548153
Norm of dx  0.0038518
Done for param alp =   0.3639; f = -739.8548
Predicted improvement:        0.038007478
lambda =          1; f =         -739.8921330
Norm of dx  0.0010189
Done for param bet =   0.9322; f = -739.8921
Predicted improvement:        0.010244437
lambda =          1; f =         -739.9023774
Norm of dx 0.00014302
Done for param delt =   0.0998; f = -739.9024
Predicted improvement:        0.004536635
lambda =          1; f =         -739.9069104
Norm of dx   0.006507
Done for param sig =   2.1157; f = -739.9069
Predicted improvement:        0.000433783
lambda =          1; f =         -739.9073438
Norm of dx  0.0021567
Done for param phi1 =   1.4037; f = -739.9073
Predicted improvement:        0.002952005
lambda =          1; f =         -739.9102978
Norm of dx   0.027824
Done for param phi2 =   5.3106; f = -739.9103
Predicted improvement:        0.135782157
lambda =          1; f =         -740.0404528
Norm of dx   0.013819
Done for param hf =   0.4751; f = -740.0405
Predicted improvement:        0.000695710
lambda =          1; f =         -740.0411475
Norm of dx  0.0035595
Done for param rhoa =   0.3569; f = -740.0411
Predicted improvement:        0.010027185
lambda =          1; f =         -740.0512160
Norm of dx   0.011971
Done for param rhov =   0.2314; f = -740.0512
Predicted improvement:        0.000398347
lambda =          1; f =         -740.0516142
Norm of dx  0.0023561
Done for param rhog =   0.6239; f = -740.0516
Predicted improvement:        0.000183503
lambda =          1; f =         -740.0517977
Norm of dx  0.0018518
Done for param rhorer =   0.5861; f = -740.0518
Sequence of univariate steps!!
Actual dxnorm 0.040465
FVAL          -740.0518
Improvement   0.5103
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  4.059254e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.31166 s.
 
Iteration 13
Correct for low angle: 6.36863e-13
Predicted improvement: 99795352211.452316284
lambda =          1; f = 1151459862608502400.0000000
lambda =    0.33333; f = 127939984652573200.0000000
lambda =    0.11111; f = 14215553823050530.0000000
lambda =   0.037037; f = 1579505971260057.0000000
lambda =   0.012346; f = 175500660446611.3125000
lambda =  0.0041152; f = 19500072373614.7226562
lambda =  0.0013717; f = 2166674371299.0927734
lambda = 0.00045725; f = 240741484084.6113892
lambda = 0.00015242; f =  26749015778.7541275
lambda = 5.0805e-05; f =   2972099761.8724585
lambda = 1.6935e-05; f =    330228506.4382821
lambda =  5.645e-06; f =     36690023.1668582
lambda = 1.8817e-06; f =      4075556.0023777
lambda = 6.2723e-07; f =       452030.5511668
lambda = 2.0908e-07; f =        49517.8392151
lambda = 6.9692e-08; f =         4827.8946922
lambda = 2.3231e-08; f =         -126.4262238
lambda = 7.7435e-09; f =         -673.3612576
lambda = 2.5812e-09; f =         -733.1125295

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =  24546007538.5474434
lambda = -2.0908e-07; f =   2727112025.5076494
lambda = -6.9692e-08; f =    302937966.4192038
lambda = -2.3231e-08; f =     33634514.7147015
lambda = -7.7435e-09; f =      3728316.0828531
lambda = -2.5812e-09; f =       410874.0555260
Norm of dx 2.4979e+11
Predicted improvement:       11.464122497
lambda =          1; f =         -739.7896573
lambda =    0.33333; f =         -740.0466480
lambda =    0.11111; f =         -740.0516813
lambda =   0.037037; f =         -721.5852291
lambda =   0.012346; f =         -738.1887476
lambda =  0.0041152; f =         -739.9076943
lambda =  0.0013717; f =         -740.0567541
lambda = 0.00045725; f =         -740.0593377
lambda = 0.00088394; f =         -740.0610630
Norm of dx     12.769
Predicted improvement:        0.072958276
lambda =          1; f =         -740.1375949
Norm of dx  0.0018489
Done for param e_a =   0.0612; f = -740.1376
Predicted improvement:        0.003036260
lambda =          1; f =         -740.1405967
Norm of dx  0.0016171
Done for param e_v =   0.2386; f = -740.1406
Predicted improvement:        0.001555859
lambda =          1; f =         -740.1421647
Norm of dx 0.00017187
Done for param e_rer =   0.0364; f = -740.1422
Predicted improvement:        0.150019871
lambda =          1; f =         -740.2952205
Norm of dx  0.0034066
Done for param alp =   0.3599; f = -740.2952
Predicted improvement:        0.024015403
lambda =          1; f =         -740.3188935
Norm of dx 0.00081199
Done for param bet =   0.9333; f = -740.3189
Predicted improvement:        0.009881817
lambda =          1; f =         -740.3287754
Norm of dx 0.00014047
Done for param delt =   0.0998; f = -740.3288
Predicted improvement:        0.004505211
lambda =          1; f =         -740.3332765
Norm of dx  0.0064052
Done for param sig =   2.1136; f = -740.3333
Predicted improvement:        0.000440607
lambda =          1; f =         -740.3337168
Norm of dx   0.002157
Done for param phi1 =   1.4039; f = -740.3337
Predicted improvement:        0.002595317
lambda =          1; f =         -740.3363138
Norm of dx   0.026193
Done for param phi2 =   5.3469; f = -740.3363
Predicted improvement:        0.113010584
lambda =          1; f =         -740.4450624
Norm of dx   0.012276
Done for param hf =   0.4872; f = -740.4451
Predicted improvement:        0.000496995
lambda =          1; f =         -740.4455588
Norm of dx  0.0030128
Done for param rhoa =   0.3540; f = -740.4456
Predicted improvement:        0.008883657
lambda =          1; f =         -740.4544650
Norm of dx     0.0112
Done for param rhov =   0.2203; f = -740.4545
Predicted improvement:        0.000408784
lambda =          1; f =         -740.4548736
Norm of dx  0.0023838
Done for param rhog =   0.6264; f = -740.4549
Predicted improvement:        0.000080258
lambda =          1; f =         -740.4549539
Norm of dx  0.0012284
Done for param rhorer =   0.5848; f = -740.4550
Sequence of univariate steps!!
Actual dxnorm 0.040412
FVAL          -740.455
Improvement   0.40316
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  3.371375e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.31829 s.
 
Iteration 14
Correct for low angle: 1.48182e-12
Predicted improvement: 34531639774.108154297
lambda =          1; f = 10049248862154946248704.0000000
lambda =    0.33333; f = 1116583206764362465280.0000000
lambda =    0.11111; f = 124064800704348372992.0000000
lambda =   0.037037; f = 13784977840289552384.0000000
lambda =   0.012346; f = 1531664199226898432.0000000
lambda =  0.0041152; f = 170184909275304320.0000000
lambda =  0.0013717; f = 18909433780620072.0000000
lambda = 0.00045725; f = 2101048003412038.0000000
lambda = 0.00015242; f = 233449713344920.2812500
lambda = 5.0805e-05; f = 25938835433906.4570312
lambda = 1.6935e-05; f = 2882085624089.8496094
lambda =  5.645e-06; f = 320229334947.9350586
lambda = 1.8817e-06; f =  35580236430.3856277
lambda = 6.2723e-07; f =   3953092242.3722248
lambda = 2.0908e-07; f =    439142918.6376627
lambda = 6.9692e-08; f =     48763374.3041327
lambda = 2.3231e-08; f =      5407625.7287789
lambda = 7.7435e-09; f =       596905.5583664
lambda = 2.5812e-09; f =        64576.1483655

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =        23819.1499311
lambda = -2.0908e-07; f =         1981.6821999
lambda = -6.9692e-08; f =         -440.0554974
lambda = -2.3231e-08; f =         -707.6166493
lambda = -7.7435e-09; f =         -736.9709328
lambda = -2.5812e-09; f =         -740.1198436
Norm of dx 1.0025e+11
Predicted improvement:        8.011104359
lambda =          1; f =         -740.2443922
lambda =    0.33333; f =         -740.4489390
lambda =    0.11111; f =         -740.4545528
lambda =   0.037037; f =         -740.4549474
lambda =   0.012346; f =         -736.1556499
lambda =  0.0041152; f =         -740.0212476
lambda =  0.0013717; f =         -740.4214177
lambda = 0.00045725; f =         -740.4561118
lambda = 0.00015242; f =         -740.4567106
lambda = 0.00029465; f =         -740.4571135
Norm of dx     8.4125
Predicted improvement:        0.057042966
lambda =          1; f =         -740.5166650
Norm of dx  0.0016955
Done for param e_a =   0.0629; f = -740.5167
Predicted improvement:        0.002583682
lambda =          1; f =         -740.5192217
Norm of dx  0.0014812
Done for param e_v =   0.2372; f = -740.5192
Predicted improvement:        0.000095052
lambda =          1; f =         -740.5193166
Norm of dx 5.6269e-05
Done for param e_g =   0.0475; f = -740.5193
Predicted improvement:        0.000761535
lambda =          1; f =         -740.5200823
Norm of dx 0.00012109
Done for param e_rer =   0.0365; f = -740.5201
Predicted improvement:        0.115388343
lambda =          1; f =         -740.6374879
Norm of dx  0.0030036
Done for param alp =   0.3567; f = -740.6375
Predicted improvement:        0.015420414
lambda =          1; f =         -740.6527459
Norm of dx 0.00065254
Done for param bet =   0.9341; f = -740.6527
Predicted improvement:        0.002768906
lambda =          1; f =         -740.6555148
Norm of dx 7.4357e-05
Done for param delt =   0.0998; f = -740.6555
Predicted improvement:        0.002742058
lambda =          1; f =         -740.6582555
Norm of dx  0.0049352
Done for param sig =   2.1096; f = -740.6583
Predicted improvement:        0.000045568
lambda =          1; f =         -740.6583010
Norm of dx 0.00068835
Done for param phi1 =   1.4043; f = -740.6583
Predicted improvement:        0.003324685
lambda =          1; f =         -740.6616281
Norm of dx   0.029744
Done for param phi2 =   5.3787; f = -740.6616
Predicted improvement:        0.090502791
lambda =          1; f =         -740.7490869
Norm of dx   0.010716
Done for param hf =   0.4979; f = -740.7491
Predicted improvement:        0.000283323
lambda =          1; f =         -740.7493699
Norm of dx  0.0022775
Done for param rhoa =   0.3518; f = -740.7494
Predicted improvement:        0.007723463
lambda =          1; f =         -740.7571017
Norm of dx   0.010361
Done for param rhov =   0.2099; f = -740.7571
Predicted improvement:        0.000407391
lambda =          1; f =         -740.7575089
Norm of dx  0.0023757
Done for param rhog =   0.6288; f = -740.7575
Predicted improvement:        0.000024504
lambda =          1; f =         -740.7575334
Norm of dx 0.00068025
Done for param rhorer =   0.5841; f = -740.7575
Sequence of univariate steps!!
Actual dxnorm 0.035697
FVAL          -740.7575
Improvement   0.30258
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  8.578000e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.33568 s.
 
Iteration 15
Correct for low angle: 4.86139e-13
Predicted improvement: 99564168066.969223022
lambda =          1; f = 113455188003952542089216.0000000
lambda =    0.33333; f = 12606131999962916978688.0000000
lambda =    0.11111; f = 1400681333170458853376.0000000
lambda =   0.037037; f = 155631259188244381696.0000000
lambda =   0.012346; f = 17292362114387877888.0000000
lambda =  0.0041152; f = 1921373562385561856.0000000
lambda =  0.0013717; f = 213485949416254112.0000000
lambda = 0.00045725; f = 23720660392943524.0000000
lambda = 0.00015242; f = 2635628714779876.5000000
lambda = 5.0805e-05; f = 292847562385313.4375000
lambda = 1.6935e-05; f = 32538593845642.6210938
lambda =  5.645e-06; f = 3615391250032.0224609
lambda = 1.8817e-06; f = 401707449747.2476196
lambda = 6.2723e-07; f =  44633264269.1964111
lambda = 2.0908e-07; f =   4958952215.0495872
lambda = 6.9692e-08; f =    550894467.8466935
lambda = 2.3231e-08; f =     61176656.2621937
lambda = 7.7435e-09; f =      6785693.5254744
lambda = 2.5812e-09; f =       749628.7804850

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =       261716.1694062
lambda = -2.0908e-07; f =        28397.3468644
lambda = -6.9692e-08; f =         2489.1907357
lambda = -2.3231e-08; f =         -384.2240816
lambda = -7.7435e-09; f =         -701.7598785
lambda = -2.5812e-09; f =         -736.6045990
Norm of dx 3.3683e+11
Predicted improvement:        8.182651762
lambda =          1; f =         -740.4988397
lambda =    0.33333; f =         -740.7520469
lambda =    0.11111; f =         -740.7574277
lambda =   0.037037; f =         -722.8903176
lambda =   0.012346; f =         -738.9070784
lambda =  0.0041152; f =         -740.5968267
lambda =  0.0013717; f =         -740.7546431
lambda = 0.00045725; f =         -740.7622009
Norm of dx     13.622
Predicted improvement:        0.043154969
lambda =          1; f =         -740.8070130
Norm of dx  0.0015256
Done for param e_a =   0.0645; f = -740.8070
Predicted improvement:        0.001577203
lambda =          1; f =         -740.8085774
Norm of dx  0.0011488
Done for param e_v =   0.2362; f = -740.8086
Predicted improvement:        0.000135467
lambda =          1; f =         -740.8087126
Norm of dx  6.711e-05
Done for param e_g =   0.0474; f = -740.8087
Predicted improvement:        0.000231532
lambda =          1; f =         -740.8089448
Norm of dx  6.716e-05
Done for param e_rer =   0.0366; f = -740.8089
Predicted improvement:        0.084826363
lambda =          1; f =         -740.8951941
Norm of dx  0.0025859
Done for param alp =   0.3539; f = -740.8952
Predicted improvement:        0.009147804
lambda =          1; f =         -740.9042645
Norm of dx 0.00050425
Done for param bet =   0.9347; f = -740.9043
Predicted improvement:        0.002606353
lambda =          1; f =         -740.9068709
Norm of dx 7.2142e-05
Done for param delt =   0.0998; f = -740.9069
Predicted improvement:        0.002653041
lambda =          1; f =         -740.9095234
Norm of dx  0.0048023
Done for param sig =   2.1069; f = -740.9095
Predicted improvement:        0.000060911
lambda =          1; f =         -740.9095843
Norm of dx 0.00079089
Done for param phi1 =   1.4050; f = -740.9096
Predicted improvement:        0.002741306
lambda =          1; f =         -740.9123274
Norm of dx   0.027108
Done for param phi2 =   5.4115; f = -740.9123
Predicted improvement:        0.071530022
lambda =          1; f =         -740.9817261
Norm of dx  0.0093116
Done for param hf =   0.5072; f = -740.9817
Predicted improvement:        0.000187243
lambda =          1; f =         -740.9819132
Norm of dx  0.0018543
Done for param rhoa =   0.3500; f = -740.9819
Predicted improvement:        0.005770619
lambda =          1; f =         -740.9876825
Norm of dx  0.0088835
Done for param rhov =   0.2011; f = -740.9877
Predicted improvement:        0.000373033
lambda =          1; f =         -740.9880554
Norm of dx  0.0022691
Done for param rhog =   0.6312; f = -740.9881
Sequence of univariate steps!!
Actual dxnorm 0.035605
FVAL          -740.9881
Improvement   0.23052
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  4.351083e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.31606 s.
 
Iteration 16
Correct for low angle: 1.04817e-12
Predicted improvement: 32556694181.728096008
lambda =          1; f = 16794549903149680820224.0000000
lambda =    0.33333; f = 1866061100166726025216.0000000
lambda =    0.11111; f = 207340122179667820544.0000000
lambda =   0.037037; f = 23037791332936593408.0000000
lambda =   0.012346; f = 2559754585761896960.0000000
lambda =  0.0041152; f = 284417173933561856.0000000
lambda =  0.0013717; f = 31601907460771596.0000000
lambda = 0.00045725; f = 3511322799840220.0000000
lambda = 0.00015242; f = 390146893974057.0625000
lambda = 5.0805e-05; f = 43349626956924.8203125
lambda = 1.6935e-05; f = 4816615907316.5556641
lambda =  5.645e-06; f = 535176441457.8869019
lambda = 1.8817e-06; f =  59463014019.2795639
lambda = 6.2723e-07; f =   6606656114.5879040
lambda = 2.0908e-07; f =    733957321.2163774
lambda = 6.9692e-08; f =     81511853.9337135
lambda = 2.3231e-08; f =      9043453.3880889
lambda = 7.7435e-09; f =       999922.0264087
lambda = 2.5812e-09; f =       109033.9785188

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =        36300.3896591
lambda = -2.0908e-07; f =         3365.7542245
lambda = -6.9692e-08; f =         -287.4584062
lambda = -2.3231e-08; f =         -691.3442452
lambda = -7.7435e-09; f =         -735.6763218
lambda = -2.5812e-09; f =         -740.4628059
Norm of dx 1.2959e+11
Predicted improvement:        6.144607321
lambda =          1; f =         -740.7826644
lambda =    0.33333; f =         -740.9844732
lambda =    0.11111; f =         -740.9879629
lambda =   0.037037; f =         -726.3255621
lambda =   0.012346; f =         -739.4602515
lambda =  0.0041152; f =         -740.8520194
lambda =  0.0013717; f =         -740.9841788
lambda = 0.00045725; f =         -740.9913708
Norm of dx      7.439
Predicted improvement:        0.031839333
lambda =          1; f =         -741.0242873
Norm of dx  0.0013497
Done for param e_a =   0.0659; f = -741.0243
Predicted improvement:        0.001083697
lambda =          1; f =         -741.0253637
Norm of dx 0.00094685
Done for param e_v =   0.2353; f = -741.0254
Predicted improvement:        0.000170986
lambda =          1; f =         -741.0255343
Norm of dx   7.53e-05
Done for param e_g =   0.0473; f = -741.0255
Predicted improvement:        0.000061626
lambda =          1; f =         -741.0255960
Norm of dx 3.4765e-05
Done for param e_rer =   0.0366; f = -741.0256
Predicted improvement:        0.060853387
lambda =          1; f =         -741.0872935
Norm of dx  0.0022007
Done for param alp =   0.3514; f = -741.0873
Predicted improvement:        0.005177572
lambda =          1; f =         -741.0924376
Norm of dx 0.00038049
Done for param bet =   0.9352; f = -741.0924
Predicted improvement:        0.002384192
lambda =          1; f =         -741.0948218
Norm of dx 6.8998e-05
Done for param delt =   0.0998; f = -741.0948
Predicted improvement:        0.002178552
lambda =          1; f =         -741.0969994
Norm of dx  0.0043092
Done for param sig =   2.1035; f = -741.0970
Predicted improvement:        0.002592019
lambda =          1; f =         -741.0995930
Norm of dx   0.026445
Done for param phi2 =   5.4411; f = -741.0996
Predicted improvement:        0.056049276
lambda =          1; f =         -741.1541694
Norm of dx  0.0080757
Done for param hf =   0.5153; f = -741.1542
Predicted improvement:        0.000105445
lambda =          1; f =         -741.1542748
Norm of dx  0.0013936
Done for param rhoa =   0.3487; f = -741.1543
Predicted improvement:        0.004456439
lambda =          1; f =         -741.1587261
Norm of dx  0.0077387
Done for param rhov =   0.1933; f = -741.1587
Predicted improvement:        0.000320773
lambda =          1; f =         -741.1590467
Norm of dx  0.0021004
Done for param rhog =   0.6333; f = -741.1590
Sequence of univariate steps!!
Actual dxnorm 0.03209
FVAL          -741.159
Improvement   0.17099
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  5.147535e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.30424 s.
 
Iteration 17
Near-singular H problem.
Correct for low angle: 1.79739e-22
Predicted improvement: 338364909033.108886719
lambda =          1; f = 50799026129515773952.0000000
lambda =    0.33333; f = 5644336236085849088.0000000
lambda =    0.11111; f = 627148470500533888.0000000
lambda =   0.037037; f = 69683163330390528.0000000
lambda =   0.012346; f = 7742573683857067.0000000
lambda =  0.0041152; f = 860285958366023.3750000
lambda =  0.0013717; f = 95587326537909.8750000
lambda = 0.00045725; f = 10620813336194.2578125
lambda = 0.00015242; f = 1180090129065.6247559
lambda = 5.0805e-05; f = 131121044479.4001312
lambda = 1.6935e-05; f =  14568977519.8190174
lambda =  5.645e-06; f =   1618765707.5072081
lambda = 1.8817e-06; f =    179859233.8676303
lambda = 6.2723e-07; f =     19982720.1134404
lambda = 2.0908e-07; f =      2219319.8183484
lambda = 6.9692e-08; f =       245825.0318762
lambda = 2.3231e-08; f =        26619.7788172
lambda = 7.7435e-09; f =         2287.5521474
lambda = 2.5812e-09; f =         -408.0614055

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =      5649332.4695906
lambda = -2.0908e-07; f =       626924.7529888
lambda = -6.9692e-08; f =        68959.8167756
lambda = -2.3231e-08; f =         6990.5012957
lambda = -7.7435e-09; f =          113.9127168
lambda = -2.5812e-09; f =         -647.3005458
Norm of dx 8.0723e+09
Predicted improvement:        4.542796914
lambda =          1; f =         -741.0383604
lambda =    0.33333; f =         -741.1575964
lambda =    0.11111; f =         -741.1589797
lambda =   0.037037; f =         -729.5459509
lambda =   0.012346; f =         -739.9436547
lambda =  0.0041152; f =         -741.0489329
lambda =  0.0013717; f =         -741.1551207
lambda = 0.00045725; f =         -741.1613801
Norm of dx     3.1988
Predicted improvement:        0.023089246
lambda =          1; f =         -741.1851238
Norm of dx  0.0011802
Done for param e_a =   0.0671; f = -741.1851
Predicted improvement:        0.000662798
lambda =          1; f =         -741.1857831
Norm of dx 0.00073662
Done for param e_v =   0.2346; f = -741.1858
Predicted improvement:        0.000201685
lambda =          1; f =         -741.1859842
Norm of dx  8.166e-05
Done for param e_g =   0.0472; f = -741.1860
Predicted improvement:        0.042245603
lambda =          1; f =         -741.2286977
Norm of dx  0.0018416
Done for param alp =   0.3493; f = -741.2287
Predicted improvement:        0.002724999
lambda =          1; f =         -741.2314103
Norm of dx 0.00027676
Done for param bet =   0.9356; f = -741.2314
Predicted improvement:        0.001961935
lambda =          1; f =         -741.2333723
Norm of dx  6.259e-05
Done for param delt =   0.0998; f = -741.2334
Predicted improvement:        0.001564311
lambda =          1; f =         -741.2349356
Norm of dx  0.0036185
Done for param sig =   2.1000; f = -741.2349
Predicted improvement:        0.000031351
lambda =          1; f =         -741.2349669
Norm of dx 0.00056103
Done for param phi1 =   1.4062; f = -741.2350
Predicted improvement:        0.002325707
lambda =          1; f =         -741.2372951
Norm of dx   0.025124
Done for param phi2 =   5.4677; f = -741.2373
Predicted improvement:        0.042070689
lambda =          1; f =         -741.2784109
Norm of dx  0.0068676
Done for param hf =   0.5222; f = -741.2784
Predicted improvement:        0.000060961
lambda =          1; f =         -741.2784719
Norm of dx  0.0010612
Done for param rhoa =   0.3477; f = -741.2785
Predicted improvement:        0.003270594
lambda =          1; f =         -741.2817379
Norm of dx  0.0065715
Done for param rhov =   0.1867; f = -741.2817
Predicted improvement:        0.000260263
lambda =          1; f =         -741.2819981
Norm of dx  0.0018887
Done for param rhog =   0.6352; f = -741.2820
Sequence of univariate steps!!
Actual dxnorm 0.028645
FVAL          -741.282
Improvement   0.12295
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.106814e-24.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.30164 s.
 
Iteration 18
Correct for low angle: 4.25935e-13
Predicted improvement: 59002943239.937217712
lambda =          1; f = 116177829369441905803264.0000000
lambda =    0.33333; f = 12908647707233830830080.0000000
lambda =    0.11111; f = 1434294189532000813056.0000000
lambda =   0.037037; f = 159366021005562314752.0000000
lambda =   0.012346; f = 17707335649435058176.0000000
lambda =  0.0041152; f = 1967481732876236288.0000000
lambda =  0.0013717; f = 218609079447398944.0000000
lambda = 0.00045725; f = 24289897055279200.0000000
lambda = 0.00015242; f = 2698877230220002.5000000
lambda = 5.0805e-05; f = 299875174346271.6875000
lambda = 1.6935e-05; f = 33319439330509.7421875
lambda =  5.645e-06; f = 3702151763272.2900391
lambda = 1.8817e-06; f = 411347474712.3329468
lambda = 6.2723e-07; f =  45704367466.9198151
lambda = 2.0908e-07; f =   5077960118.7730379
lambda = 6.9692e-08; f =    564116380.3968052
lambda = 2.3231e-08; f =     62645361.3820684
lambda = 7.7435e-09; f =      6948750.6184224
lambda = 2.5812e-09; f =       767701.8156578

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =       246554.4785864
lambda = -2.0908e-07; f =        26710.0691289
lambda = -6.9692e-08; f =         2300.5547230
lambda = -2.3231e-08; f =         -405.8036001
lambda = -7.7435e-09; f =         -704.6621462
lambda = -2.5812e-09; f =         -737.3886712
Norm of dx 3.4085e+11
Predicted improvement:        2.580214528
lambda =          1; f =         -741.2113034
lambda =    0.33333; f =         -741.2818783
lambda =    0.11111; f =         -720.4914454
lambda =   0.037037; f =         -739.0991912
lambda =   0.012346; f =         -741.0819202
lambda =  0.0041152; f =         -741.2739230
lambda =  0.0013717; f =         -741.2858189
Norm of dx     5.7065
Predicted improvement:        0.016132634
lambda =          1; f =         -741.3023458
Norm of dx  0.0010099
Done for param e_a =   0.0682; f = -741.3023
Predicted improvement:        0.000367945
lambda =          1; f =         -741.3027124
Norm of dx 0.00054695
Done for param e_v =   0.2343; f = -741.3027
Predicted improvement:        0.000198985
lambda =          1; f =         -741.3029108
Norm of dx 8.0951e-05
Done for param e_g =   0.0471; f = -741.3029
Predicted improvement:        0.000017756
lambda =          1; f =         -741.3029285
Norm of dx 1.8723e-05
Done for param e_rer =   0.0366; f = -741.3029
Predicted improvement:        0.026633829
lambda =          1; f =         -741.3298008
Norm of dx  0.0014677
Done for param alp =   0.3473; f = -741.3298
Predicted improvement:        0.001024168
lambda =          1; f =         -741.3308220
Norm of dx 0.00017003
Done for param bet =   0.9361; f = -741.3308
Predicted improvement:        0.002714010
lambda =          1; f =         -741.3335360
Norm of dx 7.3616e-05
Done for param delt =   0.0998; f = -741.3335
Predicted improvement:        0.001954458
lambda =          1; f =         -741.3354889
Norm of dx  0.0040145
Done for param sig =   2.0981; f = -741.3355
Predicted improvement:        0.001351948
lambda =          1; f =         -741.3368415
Norm of dx   0.019215
Done for param phi2 =   5.4943; f = -741.3368
Predicted improvement:        0.031732000
lambda =          1; f =         -741.3679512
Norm of dx  0.0058673
Done for param hf =   0.5281; f = -741.3680
Predicted improvement:        0.000032143
lambda =          1; f =         -741.3679834
Norm of dx 0.00077185
Done for param rhoa =   0.3469; f = -741.3680
Predicted improvement:        0.002250451
lambda =          1; f =         -741.3702292
Norm of dx  0.0054079
Done for param rhov =   0.1813; f = -741.3702
Predicted improvement:        0.000195407
lambda =          1; f =         -741.3704246
Norm of dx  0.0016337
Done for param rhog =   0.6370; f = -741.3704
Sequence of univariate steps!!
Actual dxnorm 0.028108
FVAL          -741.3704
Improvement   0.088427
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  8.008529e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.28327 s.
 
Iteration 19
Correct for low angle: 3.48929e-13
Predicted improvement: 42831013304.865371704
lambda =          1; f = 1847834283276978688.0000000
lambda =    0.33333; f = 205314920263729632.0000000
lambda =    0.11111; f = 22812768884732072.0000000
lambda =   0.037037; f = 2534752087149760.5000000
lambda =   0.012346; f = 281639117076050.8750000
lambda =  0.0041152; f = 31293233990783.7851562
lambda =  0.0013717; f = 3477025585246.9082031
lambda = 0.00045725; f = 386336037795.9705200
lambda = 0.00015242; f =  42926179874.6817856
lambda = 5.0805e-05; f =   4769559592.8377771
lambda = 1.6935e-05; f =    529945316.4802878
lambda =  5.645e-06; f =     58880463.3526636
lambda = 1.8817e-06; f =      6541056.4113075
lambda = 6.2723e-07; f =       725939.9273796
lambda = 2.0908e-07; f =        79939.8465787
lambda = 6.9692e-08; f =         8203.2323303
lambda = 2.3231e-08; f =          246.1824503
lambda = 7.7435e-09; f =         -633.4013120
lambda = 2.5812e-09; f =         -729.8985663

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =  37237590942.5176926
lambda = -2.0908e-07; f =   4137236637.3179698
lambda = -6.9692e-08; f =    459601370.9071461
lambda = -2.3231e-08; f =     51035856.3318953
lambda = -7.7435e-09; f =      5659896.6614384
lambda = -2.5812e-09; f =       624859.4293858
Norm of dx 3.0767e+11
Predicted improvement:        2.140116436
lambda =          1; f =         -741.1183716
lambda =    0.33333; f =         -741.3692847
lambda =    0.11111; f =         -741.3703739
lambda =   0.037037; f =         -731.4810190
lambda =   0.012346; f =         -740.3068686
lambda =  0.0041152; f =         -741.2639945
lambda =  0.0013717; f =         -741.3625134
lambda = 0.00045725; f =         -741.3708503
lambda = 0.00015242; f =         -741.3709068
lambda = 0.00029465; f =         -741.3710498
Norm of dx     1.5542
Predicted improvement:        0.011029154
lambda =          1; f =         -741.3823025
Norm of dx 0.00085111
Done for param e_a =   0.0690; f = -741.3823
Predicted improvement:        0.000318496
lambda =          1; f =         -741.3826199
Norm of dx 0.00050754
Done for param e_v =   0.2337; f = -741.3826
Predicted improvement:        0.000179504
lambda =          1; f =         -741.3827989
Norm of dx 7.6742e-05
Done for param e_g =   0.0471; f = -741.3828
Predicted improvement:        0.000032003
lambda =          1; f =         -741.3828309
Norm of dx 2.5131e-05
Done for param e_rer =   0.0366; f = -741.3828
Predicted improvement:        0.017184922
lambda =          1; f =         -741.4001423
Norm of dx  0.0011827
Done for param alp =   0.3461; f = -741.4001
Predicted improvement:        0.000410785
lambda =          1; f =         -741.4005523
Norm of dx 0.00010797
Done for param bet =   0.9362; f = -741.4006
Predicted improvement:        0.000716364
lambda =          1; f =         -741.4012687
Norm of dx  3.782e-05
Done for param delt =   0.0998; f = -741.4013
Predicted improvement:        0.001252512
lambda =          1; f =         -741.4025204
Norm of dx  0.0031902
Done for param sig =   2.0945; f = -741.4025
Predicted improvement:        0.000058529
lambda =          1; f =         -741.4025790
Norm of dx  0.0007604
Done for param phi1 =   1.4078; f = -741.4026
Predicted improvement:        0.001399291
lambda =          1; f =         -741.4039789
Norm of dx   0.019591
Done for param phi2 =   5.5142; f = -741.4040
Predicted improvement:        0.022649209
lambda =          1; f =         -741.4262544
Norm of dx  0.0048865
Done for param hf =   0.5331; f = -741.4263
Predicted improvement:        0.000014129
lambda =          1; f =         -741.4262685
Norm of dx 0.00051241
Done for param rhoa =   0.3464; f = -741.4263
Predicted improvement:        0.001661863
lambda =          1; f =         -741.4279267
Norm of dx  0.0046102
Done for param rhov =   0.1767; f = -741.4279
Predicted improvement:        0.000154656
lambda =          1; f =         -741.4280814
Norm of dx  0.0014513
Done for param rhog =   0.6384; f = -741.4281
Predicted improvement:        0.000012326
lambda =          1; f =         -741.4280937
Norm of dx 0.00048383
Done for param rhorer =   0.5845; f = -741.4281
Sequence of univariate steps!!
Actual dxnorm 0.021413
FVAL          -741.4281
Improvement   0.057669
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.609323e-24.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.46688 s.
 
Iteration 20
Near-singular H problem.
Correct for low angle: 6.83321e-22
Predicted improvement: 172770174659.516235352
lambda =          1; f = 45927193179961311232.0000000
lambda =    0.33333; f = 5103021463944708096.0000000
lambda =    0.11111; f = 567002384717599424.0000000
lambda =   0.037037; f = 63000264913573080.0000000
lambda =   0.012346; f = 7000029416491329.0000000
lambda =  0.0041152; f = 777781040159673.6250000
lambda =  0.0013717; f = 86420113533817.3750000
lambda = 0.00045725; f = 9602234156831.4121094
lambda = 0.00015242; f = 1066914679128.6131592
lambda = 5.0805e-05; f = 118545999297.2212982
lambda = 1.6935e-05; f =  13171751879.6692944
lambda =  5.645e-06; f =   1463518946.9234889
lambda = 1.8817e-06; f =    162609769.9970658
lambda = 6.2723e-07; f =     18066170.3536853
lambda = 2.0908e-07; f =      2006388.2453701
lambda = 6.9692e-08; f =       222171.9198648
lambda = 2.3231e-08; f =        23993.5332760
lambda = 7.7435e-09; f =         1996.2095032
lambda = 2.5812e-09; f =         -440.4465628

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =      4981754.3823487
lambda = -2.0908e-07; f =       552740.1672425
lambda = -6.9692e-08; f =        60713.8566505
lambda = -2.3231e-08; f =         6073.0599099
lambda = -7.7435e-09; f =           11.4570257
lambda = -2.5812e-09; f =         -658.9879728
Norm of dx 7.6546e+09
Predicted improvement:        1.729215632
lambda =          1; f =         -741.4170886
lambda =    0.33333; f =         -741.4271432
lambda =    0.11111; f =         -741.4280560
lambda =   0.037037; f =         -732.4632933
lambda =   0.012346; f =         -740.4606029
lambda =  0.0041152; f =         -741.3300865
lambda =  0.0013717; f =         -741.4203668
lambda = 0.00045725; f =         -741.4282894
lambda = 0.00015242; f =         -741.4284668
lambda = 0.00029465; f =         -741.4285373
Norm of dx     7.1176
Predicted improvement:        0.007374163
lambda =          1; f =         -741.4360351
Norm of dx 0.00070717
Done for param e_a =   0.0698; f = -741.4360
Predicted improvement:        0.000148904
lambda =          1; f =         -741.4361836
Norm of dx 0.00034601
Done for param e_v =   0.2334; f = -741.4362
Predicted improvement:        0.000168373
lambda =          1; f =         -741.4363516
Norm of dx 7.4197e-05
Done for param e_g =   0.0470; f = -741.4364
Predicted improvement:        0.000063208
lambda =          1; f =         -741.4364147
Norm of dx 3.5312e-05
Done for param e_rer =   0.0365; f = -741.4364
Predicted improvement:        0.010884275
lambda =          1; f =         -741.4473612
Norm of dx 0.00094387
Done for param alp =   0.3451; f = -741.4474
Predicted improvement:        0.000119131
lambda =          1; f =         -741.4474802
Norm of dx 5.8283e-05
Done for param bet =   0.9363; f = -741.4475
Predicted improvement:        0.000603907
lambda =          1; f =         -741.4480841
Norm of dx 3.4725e-05
Done for param delt =   0.0998; f = -741.4481
Predicted improvement:        0.000997407
lambda =          1; f =         -741.4490811
Norm of dx  0.0028297
Done for param sig =   2.0919; f = -741.4491
Predicted improvement:        0.000033226
lambda =          1; f =         -741.4491143
Norm of dx 0.00057122
Done for param phi1 =   1.4091; f = -741.4491
Predicted improvement:        0.000955847
lambda =          1; f =         -741.4500705
Norm of dx   0.016227
Done for param phi2 =   5.5324; f = -741.4501
Predicted improvement:        0.015632368
lambda =          1; f =         -741.4654894
Norm of dx  0.0040096
Done for param hf =   0.5372; f = -741.4655
Predicted improvement:        0.001032739
lambda =          1; f =         -741.4665201
Norm of dx  0.0036089
Done for param rhov =   0.1731; f = -741.4665
Predicted improvement:        0.000110847
lambda =          1; f =         -741.4666309
Norm of dx   0.001227
Done for param rhog =   0.6397; f = -741.4666
Sequence of univariate steps!!
Actual dxnorm 0.019283
FVAL          -741.4666
Improvement   0.038537
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.075787e-24.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.36297 s.
 
Iteration 21
Near-singular H problem.
Correct for low angle: 6.3425e-22
Predicted improvement: 91187536306.039199829
lambda =          1; f = 20347305441583505408.0000000
lambda =    0.33333; f = 2260811715401114880.0000000
lambda =    0.11111; f = 251201301601106048.0000000
lambda =   0.037037; f = 27911255696746168.0000000
lambda =   0.012346; f = 3101250620734674.5000000
lambda =  0.0041152; f = 344583398224379.3750000
lambda =  0.0013717; f = 38287042886895.9843750
lambda = 0.00045725; f = 4254115422469.8798828
lambda = 0.00015242; f = 472679339668.7676392
lambda = 5.0805e-05; f =  52519875624.7777100
lambda = 1.6935e-05; f =   5835524301.3681707
lambda =  5.645e-06; f =    648385344.4166130
lambda = 1.8817e-06; f =     72040301.4533112
lambda = 6.2723e-07; f =      8003206.3377065
lambda = 2.0908e-07; f =       888383.2189920
lambda = 6.9692e-08; f =        97983.1300046
lambda = 2.3231e-08; f =        10206.0675973
lambda = 7.7435e-09; f =          468.0101530
lambda = 2.5812e-09; f =         -609.0278635

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =      2180408.5228037
lambda = -2.0908e-07; f =       241518.8016795
lambda = -6.9692e-08; f =        26146.9847938
lambda = -2.3231e-08; f =         2236.7036738
lambda = -7.7435e-09; f =         -413.3667723
lambda = -2.5812e-09; f =         -705.7480642
Norm of dx 5.0885e+09
Predicted improvement:        1.096484484
lambda =          1; f =         -741.2310563
lambda =    0.33333; f =         -741.4659561
lambda =    0.11111; f =         -741.4666105
lambda =   0.037037; f =         -735.0384194
lambda =   0.012346; f =         -740.7704166
lambda =  0.0041152; f =         -741.3952896
lambda =  0.0013717; f =         -741.4607095
lambda = 0.00045725; f =         -741.4666415
lambda = 0.00015242; f =         -741.4668549
Norm of dx     1.5284
Predicted improvement:        0.004731283
lambda =          1; f =         -741.4716500
Norm of dx 0.00057404
Done for param e_a =   0.0703; f = -741.4717
Predicted improvement:        0.000090711
lambda =          1; f =         -741.4717406
Norm of dx 0.00026953
Done for param e_v =   0.2332; f = -741.4717
Predicted improvement:        0.000139114
lambda =          1; f =         -741.4718793
Norm of dx 6.7321e-05
Done for param e_g =   0.0469; f = -741.4719
Predicted improvement:        0.000075183
lambda =          1; f =         -741.4719544
Norm of dx 3.8481e-05
Done for param e_rer =   0.0365; f = -741.4720
Predicted improvement:        0.006551200
lambda =          1; f =         -741.4785353
Norm of dx 0.00073402
Done for param alp =   0.3443; f = -741.4785
Predicted improvement:        0.000016023
lambda =          1; f =         -741.4785513
Norm of dx 2.1423e-05
Done for param bet =   0.9363; f = -741.4786
Predicted improvement:        0.000133903
lambda =          1; f =         -741.4786852
Norm of dx 1.6351e-05
Done for param delt =   0.0999; f = -741.4787
Predicted improvement:        0.000698373
lambda =          1; f =         -741.4793834
Norm of dx  0.0023556
Done for param sig =   2.0894; f = -741.4794
Predicted improvement:        0.000072364
lambda =          1; f =         -741.4794558
Norm of dx 0.00084092
Done for param phi1 =   1.4101; f = -741.4795
Predicted improvement:        0.000766555
lambda =          1; f =         -741.4802226
Norm of dx   0.014556
Done for param phi2 =   5.5470; f = -741.4802
Predicted improvement:        0.010862381
lambda =          1; f =         -741.4909617
Norm of dx  0.0033086
Done for param hf =   0.5406; f = -741.4910
Predicted improvement:        0.000017642
lambda =          1; f =         -741.4909793
Norm of dx 0.00057403
Done for param rhoa =   0.3458; f = -741.4910
Predicted improvement:        0.000731755
lambda =          1; f =         -741.4917097
Norm of dx  0.0030193
Done for param rhov =   0.1700; f = -741.4917
Predicted improvement:        0.000083417
lambda =          1; f =         -741.4917931
Norm of dx  0.0010631
Done for param rhog =   0.6408; f = -741.4918
Predicted improvement:        0.000035944
lambda =          1; f =         -741.4918291
Norm of dx 0.00082574
Done for param rhorer =   0.5853; f = -741.4918
Sequence of univariate steps!!
Actual dxnorm 0.015687
FVAL          -741.4918
Improvement   0.025198
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  7.699777e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.40239 s.
 
Iteration 22
Near-singular H problem.
Correct for low angle: 5.15579e-22
Predicted improvement: 217824090768.457702637
lambda =          1; f = 179038978809968263168.0000000
lambda =    0.33333; f = 19893219866807439360.0000000
lambda =    0.11111; f = 2210357762656331264.0000000
lambda =   0.037037; f = 245595306854389792.0000000
lambda =   0.012346; f = 27288367392456864.0000000
lambda =  0.0041152; f = 3032040809447445.0000000
lambda =  0.0013717; f = 336893419292626.6875000
lambda = 0.00045725; f = 37432600816755.9218750
lambda = 0.00015242; f = 4159177425805.9208984
lambda = 5.0805e-05; f = 462130677082.6034546
lambda = 1.6935e-05; f =  51347803240.7581711
lambda =  5.645e-06; f =   5705294448.9498177
lambda = 1.8817e-06; f =    633915498.4437823
lambda = 6.2723e-07; f =     70432586.7030433
lambda = 2.0908e-07; f =      7824586.5997874
lambda = 6.9692e-08; f =       868541.5702909
lambda = 2.3231e-08; f =        95780.1485878
lambda = 7.7435e-09; f =         9961.8211595
lambda = 2.5812e-09; f =          441.0218176

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =     19220983.5456873
lambda = -2.0908e-07; f =      2134738.1080649
lambda = -6.9692e-08; f =       236445.1631916
lambda = -2.3231e-08; f =        25583.3150116
lambda = -7.7435e-09; f =         2174.0841492
lambda = -2.5812e-09; f =         -420.3644018
Norm of dx 1.5096e+10
Predicted improvement:        0.974588541
lambda =          1; f =         -741.4864151
lambda =    0.33333; f =         -741.4914135
lambda =    0.11111; f =         -741.4918220
lambda =   0.037037; f =         -736.9737454
lambda =   0.012346; f =         -741.0058970
lambda =  0.0041152; f =         -741.4431852
lambda =  0.0013717; f =         -741.4882067
lambda = 0.00045725; f =         -741.4920208
lambda = 0.00015242; f =         -741.4920484
lambda = 0.00029465; f =         -741.4921129
Norm of dx     4.1002
Predicted improvement:        0.003130597
lambda =          1; f =         -741.4952780
Norm of dx 0.00047191
Done for param e_a =   0.0708; f = -741.4953
Predicted improvement:        0.000051346
lambda =          1; f =         -741.4953292
Norm of dx 0.00020249
Done for param e_v =   0.2330; f = -741.4953
Predicted improvement:        0.000111585
lambda =          1; f =         -741.4954406
Norm of dx 6.0194e-05
Done for param e_g =   0.0468; f = -741.4954
Predicted improvement:        0.000075788
lambda =          1; f =         -741.4955162
Norm of dx 3.8598e-05
Done for param e_rer =   0.0365; f = -741.4955
Predicted improvement:        0.003938962
lambda =          1; f =         -741.4994689
Norm of dx 0.00057025
Done for param alp =   0.3438; f = -741.4995
Predicted improvement:        0.000309061
lambda =          1; f =         -741.4997780
Norm of dx 2.4841e-05
Done for param delt =   0.0999; f = -741.4998
Predicted improvement:        0.000561704
lambda =          1; f =         -741.5003395
Norm of dx  0.0021035
Done for param sig =   2.0872; f = -741.5003
Predicted improvement:        0.000050627
lambda =          1; f =         -741.5003901
Norm of dx  0.0007019
Done for param phi1 =   1.4113; f = -741.5004
Predicted improvement:        0.000492624
lambda =          1; f =         -741.5008829
Norm of dx   0.011687
Done for param phi2 =   5.5598; f = -741.5009
Predicted improvement:        0.007138702
lambda =          1; f =         -741.5079562
Norm of dx  0.0026587
Done for param hf =   0.5433; f = -741.5080
Predicted improvement:        0.000442695
lambda =          1; f =         -741.5083981
Norm of dx  0.0023362
Done for param rhov =   0.1677; f = -741.5084
Predicted improvement:        0.000052964
lambda =          1; f =         -741.5084511
Norm of dx 0.00084621
Done for param rhog =   0.6416; f = -741.5085
Sequence of univariate steps!!
Actual dxnorm 0.013564
FVAL          -741.5085
Improvement   0.016622
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  5.665469e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.33074 s.
 
Iteration 23
Correct for low angle: 7.1595e-13
Predicted improvement: 6063041163.537143707
lambda =          1; f = 11784883568499524370432.0000000
lambda =    0.33333; f = 1309431507457563688960.0000000
lambda =    0.11111; f = 145492389666342354944.0000000
lambda =   0.037037; f = 16165821056983111680.0000000
lambda =   0.012346; f = 1796202333979815168.0000000
lambda =  0.0041152; f = 199578035213876352.0000000
lambda =  0.0013717; f = 22175336614321820.0000000
lambda = 0.00045725; f = 2463926079924963.0000000
lambda = 0.00015242; f = 273769494250599.8125000
lambda = 5.0805e-05; f = 30418809298915.0664062
lambda = 1.6935e-05; f = 3379859900920.4575195
lambda =  5.645e-06; f = 375537388910.8579102
lambda = 1.8817e-06; f =  41725509420.1645508
lambda = 6.2723e-07; f =   4635878238.6833973
lambda = 2.0908e-07; f =    515000657.6233621
lambda = 6.9692e-08; f =     57189554.0921271
lambda = 2.3231e-08; f =      6343048.1039778
lambda = 7.7435e-09; f =       700567.5298327
lambda = 2.5812e-09; f =        76002.2362657

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =        23703.6911890
lambda = -2.0908e-07; f =         1965.0159679
lambda = -6.9692e-08; f =         -443.6338815
lambda = -2.3231e-08; f =         -709.1489505
lambda = -7.7435e-09; f =         -738.0880302
lambda = -2.5812e-09; f =         -741.1834962
Norm of dx 1.0856e+11
Predicted improvement:        0.656393046
lambda =          1; f =         -741.1945968
lambda =    0.33333; f =         -741.5080890
lambda =    0.11111; f =         -741.5084463
lambda =   0.037037; f =         -737.4966934
lambda =   0.012346; f =         -741.0734817
lambda =  0.0041152; f =         -741.4637220
lambda =  0.0013717; f =         -741.5046817
lambda = 0.00045725; f =         -741.5084325
lambda = 0.00015242; f =         -741.5085824
Norm of dx     1.3665
Predicted improvement:        0.001881629
lambda =          1; f =         -741.5104802
Norm of dx 0.00036914
Done for param e_a =   0.0712; f = -741.5105
Predicted improvement:        0.000032033
lambda =          1; f =         -741.5105122
Norm of dx 0.00015975
Done for param e_v =   0.2328; f = -741.5105
Predicted improvement:        0.000082615
lambda =          1; f =         -741.5105947
Norm of dx 5.1714e-05
Done for param e_g =   0.0468; f = -741.5106
Predicted improvement:        0.000079795
lambda =          1; f =         -741.5106743
Norm of dx 3.9566e-05
Done for param e_rer =   0.0364; f = -741.5107
Predicted improvement:        0.002165221
lambda =          1; f =         -741.5128452
Norm of dx  0.0004235
Done for param alp =   0.3434; f = -741.5128
Predicted improvement:        0.000024071
lambda =          1; f =         -741.5128692
Norm of dx 2.6346e-05
Done for param bet =   0.9363; f = -741.5129
Predicted improvement:        0.000079600
lambda =          1; f =         -741.5129488
Norm of dx 1.2607e-05
Done for param delt =   0.0999; f = -741.5129
Predicted improvement:        0.000347109
lambda =          1; f =         -741.5132959
Norm of dx  0.0016477
Done for param sig =   2.0854; f = -741.5133
Predicted improvement:        0.000059518
lambda =          1; f =         -741.5133554
Norm of dx 0.00075973
Done for param phi1 =   1.4121; f = -741.5134
Predicted improvement:        0.000365601
lambda =          1; f =         -741.5137211
Norm of dx    0.01008
Done for param phi2 =   5.5699; f = -741.5137
Predicted improvement:        0.004751819
lambda =          1; f =         -741.5184374
Norm of dx   0.002154
Done for param hf =   0.5455; f = -741.5184
Predicted improvement:        0.000295514
lambda =          1; f =         -741.5187326
Norm of dx  0.0019004
Done for param rhov =   0.1658; f = -741.5187
Predicted improvement:        0.000040468
lambda =          1; f =         -741.5187730
Norm of dx 0.00073904
Done for param rhog =   0.6424; f = -741.5188
Predicted improvement:        0.000033662
lambda =          1; f =         -741.5188067
Norm of dx 0.00079848
Done for param rhorer =   0.5861; f = -741.5188
Sequence of univariate steps!!
Actual dxnorm 0.010736
FVAL          -741.5188
Improvement   0.010356
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  5.941171e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.3009 s.
 
Iteration 24
Correct for low angle: 6.48837e-13
Predicted improvement: 6287800530.639841080
lambda =          1; f = 19940964535305689366528.0000000
lambda =    0.33333; f = 2215662725945411764224.0000000
lambda =    0.11111; f = 246184747260713107456.0000000
lambda =   0.037037; f = 27353860784560947200.0000000
lambda =   0.012346; f = 3039317857556231168.0000000
lambda =  0.0041152; f = 337701981707919232.0000000
lambda =  0.0013717; f = 37522441590325464.0000000
lambda = 0.00045725; f = 4169159902813889.5000000
lambda = 0.00015242; f = 463239897904802.3750000
lambda = 5.0805e-05; f = 51471069334526.5625000
lambda = 1.6935e-05; f = 5718997559184.7910156
lambda =  5.645e-06; f = 635440791265.2989502
lambda = 1.8817e-06; f =  70603404605.9573975
lambda = 6.2723e-07; f =   7844446382.2038565
lambda = 2.0908e-07; f =    871479270.1263365
lambda = 6.9692e-08; f =     96788635.8561753
lambda = 2.3231e-08; f =     10739728.4186628
lambda = 7.7435e-09; f =      1188015.0812392
lambda = 2.5812e-09; f =       129805.5762074

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =        39266.6029731
lambda = -2.0908e-07; f =         3691.2857179
lambda = -6.9692e-08; f =         -252.8114128
lambda = -2.3231e-08; f =         -688.2805947
lambda = -7.7435e-09; f =         -735.8407425
lambda = -2.5812e-09; f =         -740.9598840
Norm of dx 1.4121e+11
Predicted improvement:        0.630695135
lambda =          1; f =         -741.5122319
lambda =    0.33333; f =         -741.5185234
lambda =    0.11111; f =         -741.5188045
lambda =   0.037037; f =         -738.1385201
lambda =   0.012346; f =         -741.1536051
lambda =  0.0041152; f =         -741.4816895
lambda =  0.0013717; f =         -741.5158361
lambda = 0.00045725; f =         -741.5188611
lambda = 0.00015242; f =         -741.5189409
Norm of dx     1.7912
Predicted improvement:        0.001170804
lambda =          1; f =         -741.5201197
Norm of dx 0.00029318
Done for param e_a =   0.0715; f = -741.5201
Predicted improvement:        0.000017973
lambda =          1; f =         -741.5201376
Norm of dx 0.00011955
Done for param e_v =   0.2327; f = -741.5201
Predicted improvement:        0.000064275
lambda =          1; f =         -741.5202018
Norm of dx 4.5555e-05
Done for param e_g =   0.0468; f = -741.5202
Predicted improvement:        0.000064908
lambda =          1; f =         -741.5202666
Norm of dx 3.5641e-05
Done for param e_rer =   0.0364; f = -741.5203
Predicted improvement:        0.001205582
lambda =          1; f =         -741.5214745
Norm of dx 0.00031641
Done for param alp =   0.3431; f = -741.5215
Predicted improvement:        0.000055934
lambda =          1; f =         -741.5215305
Norm of dx 4.0218e-05
Done for param bet =   0.9363; f = -741.5215
Predicted improvement:        0.000065271
lambda =          1; f =         -741.5215958
Norm of dx 1.1416e-05
Done for param delt =   0.0999; f = -741.5216
Predicted improvement:        0.000256457
lambda =          1; f =         -741.5218522
Norm of dx  0.0014121
Done for param sig =   2.0838; f = -741.5219
Predicted improvement:        0.000047316
lambda =          1; f =         -741.5218995
Norm of dx 0.00067651
Done for param phi1 =   1.4129; f = -741.5219
Predicted improvement:        0.000247046
lambda =          1; f =         -741.5221466
Norm of dx  0.0082946
Done for param phi2 =   5.5783; f = -741.5221
Predicted improvement:        0.003067977
lambda =          1; f =         -741.5251962
Norm of dx  0.0017207
Done for param hf =   0.5473; f = -741.5252
Predicted improvement:        0.000184219
lambda =          1; f =         -741.5253802
Norm of dx  0.0014951
Done for param rhov =   0.1643; f = -741.5254
Predicted improvement:        0.000027444
lambda =          1; f =         -741.5254077
Norm of dx 0.00060815
Done for param rhog =   0.6430; f = -741.5254
Sequence of univariate steps!!
Actual dxnorm 0.0089821
FVAL          -741.5254
Improvement   0.006601
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  5.834392e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.43553 s.
 
Iteration 25
Near-singular H problem.
Correct for low angle: 9.09771e-22
Predicted improvement: 65912929522.043121338
lambda =          1; f = 75125528136762163200.0000000
lambda =    0.33333; f = 8347280903492011008.0000000
lambda =    0.11111; f = 927475655745998976.0000000
lambda =   0.037037; f = 103052850572591184.0000000
lambda =   0.012346; f = 11450316708336450.0000000
lambda =  0.0041152; f = 1272257404719803.0000000
lambda =  0.0013717; f = 141361931418132.5625000
lambda = 0.00045725; f = 15706880455040.9667969
lambda = 0.00015242; f = 1745208667802.8388672
lambda = 5.0805e-05; f = 193911983218.9165039
lambda = 1.6935e-05; f =  21545745153.2174110
lambda =  5.645e-06; f =   2393960997.4340510
lambda = 1.8817e-06; f =    265991671.3710430
lambda = 6.2723e-07; f =     29552865.5572378
lambda = 2.0908e-07; f =      3282628.8387606
lambda = 6.9692e-08; f =       363956.9369239
lambda = 2.3231e-08; f =        39740.9429211
lambda = 7.7435e-09; f =         3743.8637831
lambda = 2.5812e-09; f =         -246.9984094

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =      7857764.7979174
lambda = -2.0908e-07; f =       872222.8874950
lambda = -6.9692e-08; f =        96188.7181090
lambda = -2.3231e-08; f =        10007.5727479
lambda = -7.7435e-09; f =          446.2079122
lambda = -2.5812e-09; f =         -611.4266360
Norm of dx 9.7521e+09
Predicted improvement:        0.508472452
lambda =          1; f =         -740.7761183
lambda =    0.33333; f =         -741.5253041
lambda =    0.11111; f =         -724.5534415
lambda =   0.037037; f =         -739.6645406
lambda =   0.012346; f =         -741.3269973
lambda =  0.0041152; f =         -741.5061513
lambda =  0.0013717; f =         -741.5241980
lambda = 0.00045725; f =         -741.5255833
Norm of dx     2.3038
Predicted improvement:        0.000699249
lambda =          1; f =         -741.5262862
Norm of dx 0.00022779
Done for param e_a =   0.0717; f = -741.5263
Predicted improvement:        0.000012098
lambda =          1; f =         -741.5262983
Norm of dx 9.8001e-05
Done for param e_v =   0.2326; f = -741.5263
Predicted improvement:        0.000050756
lambda =          1; f =         -741.5263490
Norm of dx 4.0436e-05
Done for param e_g =   0.0467; f = -741.5263
Predicted improvement:        0.000062615
lambda =          1; f =         -741.5264115
Norm of dx 3.4973e-05
Done for param e_rer =   0.0364; f = -741.5264
Predicted improvement:        0.000603808
lambda =          1; f =         -741.5270161
Norm of dx 0.00022418
Done for param alp =   0.3429; f = -741.5270
Predicted improvement:        0.000071765
lambda =          1; f =         -741.5270879
Norm of dx  4.561e-05
Done for param bet =   0.9362; f = -741.5271
Predicted improvement:        0.000265841
lambda =          1; f =         -741.5273538
Norm of dx 2.3038e-05
Done for param delt =   0.0999; f = -741.5274
Predicted improvement:        0.000111299
lambda =          1; f =         -741.5274651
Norm of dx 0.00092785
Done for param sig =   2.0822; f = -741.5275
Predicted improvement:        0.000071723
lambda =          1; f =         -741.5275368
Norm of dx 0.00083193
Done for param phi1 =   1.4137; f = -741.5275
Predicted improvement:        0.000172874
lambda =          1; f =         -741.5277097
Norm of dx  0.0069439
Done for param phi2 =   5.5845; f = -741.5277
Predicted improvement:        0.001901417
lambda =          1; f =         -741.5296022
Norm of dx  0.0013481
Done for param hf =   0.5487; f = -741.5296
Predicted improvement:        0.000123905
lambda =          1; f =         -741.5297260
Norm of dx  0.0012224
Done for param rhov =   0.1630; f = -741.5297
Predicted improvement:        0.000016653
lambda =          1; f =         -741.5297427
Norm of dx 0.00047342
Done for param rhog =   0.6435; f = -741.5297
Predicted improvement:        0.000024225
lambda =          1; f =         -741.5297669
Norm of dx 0.00067686
Done for param rhorer =   0.5868; f = -741.5298
Sequence of univariate steps!!
Actual dxnorm 0.0067851
FVAL          -741.5298
Improvement   0.0043592
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  4.059286e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.3979 s.
 
Iteration 26
Correct for low angle: 1.18739e-12
Predicted improvement: 2342018981.054466724
lambda =          1; f = 151052407211185696.0000000
lambda =    0.33333; f = 16783600776366612.0000000
lambda =    0.11111; f = 1864844522414891.7500000
lambda =   0.037037; f = 207204944170386.9062500
lambda =   0.012346; f = 23022770652522.2421875
lambda =  0.0041152; f = 2558085320303.7148438
lambda =  0.0013717; f = 284231599238.5371704
lambda = 0.00045725; f =  31581254032.4774780
lambda = 0.00015242; f =   3509016202.5686460
lambda = 5.0805e-05; f =    389886248.6946062
lambda = 1.6935e-05; f =     43318781.2435929
lambda =  5.645e-06; f =      4812126.2242783
lambda = 1.8817e-06; f =       533884.9934378
lambda = 6.2723e-07; f =        58616.4697718
lambda = 2.0908e-07; f =         5839.3860750
lambda = 6.9692e-08; f =          -14.7144237
lambda = 2.3231e-08; f =         -661.9207415
lambda = 7.7435e-09; f =         -733.0178716
lambda = 2.5812e-09; f =         -740.6807592

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =   2927046258.1473207
lambda = -2.0908e-07; f =    325150219.4670295
lambda = -6.9692e-08; f =     36101654.8554464
lambda = -2.3231e-08; f =      4002145.9726277
lambda = -7.7435e-09; f =       441199.8374297
lambda = -2.5812e-09; f =        47427.7609918
Norm of dx 8.6262e+10
Predicted improvement:        0.426042388
lambda =          1; f =         -741.5281018
lambda =    0.33333; f =         -741.5297020
lambda =    0.11111; f =         -728.9550058
lambda =   0.037037; f =         -740.1546289
lambda =   0.012346; f =         -741.3840119
lambda =  0.0041152; f =         -741.5159104
lambda =  0.0013717; f =         -741.5290065
lambda = 0.00045725; f =         -741.5299421
Norm of dx     1.9968
Predicted improvement:        0.000406289
lambda =          1; f =         -741.5303501
Norm of dx 0.00017437
Done for param e_a =   0.0719; f = -741.5304
Predicted improvement:        0.000031814
lambda =          1; f =         -741.5303819
Norm of dx 3.1978e-05
Done for param e_g =   0.0467; f = -741.5304
Predicted improvement:        0.000046016
lambda =          1; f =         -741.5304278
Norm of dx 2.9948e-05
Done for param e_rer =   0.0363; f = -741.5304
Predicted improvement:        0.000305721
lambda =          1; f =         -741.5307338
Norm of dx 0.00015965
Done for param alp =   0.3429; f = -741.5307
Predicted improvement:        0.000079692
lambda =          1; f =         -741.5308136
Norm of dx 4.8116e-05
Done for param bet =   0.9361; f = -741.5308
Predicted improvement:        0.000202975
lambda =          1; f =         -741.5310166
Norm of dx 2.0131e-05
Done for param delt =   0.0999; f = -741.5310
Predicted improvement:        0.000083553
lambda =          1; f =         -741.5311001
Norm of dx 0.00080245
Done for param sig =   2.0813; f = -741.5311
Predicted improvement:        0.000019028
lambda =          1; f =         -741.5311191
Norm of dx 0.00042825
Done for param phi1 =   1.4147; f = -741.5311
Predicted improvement:        0.000097651
lambda =          1; f =         -741.5312168
Norm of dx  0.0052228
Done for param phi2 =   5.5905; f = -741.5312
Predicted improvement:        0.001084282
lambda =          1; f =         -741.5322974
Norm of dx   0.001014
Done for param hf =   0.5498; f = -741.5323
Predicted improvement:        0.000051264
lambda =          1; f =         -741.5323486
Norm of dx 0.00078455
Done for param rhov =   0.1622; f = -741.5323
Sequence of univariate steps!!
Actual dxnorm 0.0062298
FVAL          -741.5323
Improvement   0.0025817
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  2.849644e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.22053 s.
 
Iteration 27
Near-singular H problem.
Correct for low angle: 2.25489e-21
Predicted improvement: 26118976886.223682404
lambda =          1; f = 33192708394390798336.0000000
lambda =    0.33333; f = 3688078710129098240.0000000
lambda =    0.11111; f = 409786523228087296.0000000
lambda =   0.037037; f = 45531835874368128.0000000
lambda =   0.012346; f = 5059092861641428.0000000
lambda =  0.0041152; f = 562121424641388.4375000
lambda =  0.0013717; f = 62457934594208.1406250
lambda = 0.00045725; f = 6939770017682.2832031
lambda = 0.00015242; f = 771085392825.5887451
lambda = 5.0805e-05; f =  85676099427.2752228
lambda = 1.6935e-05; f =   9519547726.4208565
lambda =  5.645e-06; f =   1057720800.0527556
lambda = 1.8817e-06; f =    117521858.7356121
lambda = 6.2723e-07; f =     13056659.8675522
lambda = 2.0908e-07; f =      1449861.6232190
lambda = 6.9692e-08; f =       160364.0922274
lambda = 2.3231e-08; f =        17135.4902099
lambda = 7.7435e-09; f =         1237.4705120
lambda = 2.5812e-09; f =         -523.7185226

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =      2969801.4296365
lambda = -2.0908e-07; f =       329183.5968351
lambda = -6.9692e-08; f =        35873.8403724
lambda = -2.3231e-08; f =         3313.4390247
lambda = -7.7435e-09; f =         -294.9401060
lambda = -2.5812e-09; f =         -692.9707357
Norm of dx 6.3832e+09
Predicted improvement:        0.307040259
lambda =          1; f =         -741.5311266
lambda =    0.33333; f =         -741.5323468
lambda =    0.11111; f =         -737.6519412
lambda =   0.037037; f =         -741.1165258
lambda =   0.012346; f =         -741.4912047
lambda =  0.0041152; f =         -741.5294620
lambda =  0.0013717; f =         -741.5325895
lambda = 0.00045725; f =         -741.5325626
lambda = 0.00088394; f =         -741.5326416
Norm of dx    0.84359
Predicted improvement:        0.000228254
lambda =          1; f =         -741.5328706
Norm of dx  0.0001311
Done for param e_a =   0.0720; f = -741.5329
Predicted improvement:        0.000012138
lambda =          1; f =         -741.5328827
Norm of dx 9.8132e-05
Done for param e_v =   0.2325; f = -741.5329
Predicted improvement:        0.000020128
lambda =          1; f =         -741.5329028
Norm of dx 2.5414e-05
Done for param e_g =   0.0467; f = -741.5329
Predicted improvement:        0.000030982
lambda =          1; f =         -741.5329338
Norm of dx  2.455e-05
Done for param e_rer =   0.0363; f = -741.5329
Predicted improvement:        0.000159487
lambda =          1; f =         -741.5330934
Norm of dx  0.0001154
Done for param alp =   0.3429; f = -741.5331
Predicted improvement:        0.000063995
lambda =          1; f =         -741.5331574
Norm of dx 4.3165e-05
Done for param bet =   0.9360; f = -741.5332
Predicted improvement:        0.000208445
lambda =          1; f =         -741.5333659
Norm of dx   2.04e-05
Done for param delt =   0.0999; f = -741.5334
Predicted improvement:        0.000044976
lambda =          1; f =         -741.5334108
Norm of dx 0.00058785
Done for param sig =   2.0802; f = -741.5334
Predicted improvement:        0.000034257
lambda =          1; f =         -741.5334451
Norm of dx  0.0030948
Done for param phi2 =   5.5938; f = -741.5334
Predicted improvement:        0.000506252
lambda =          1; f =         -741.5339502
Norm of dx 0.00069062
Done for param hf =   0.5506; f = -741.5340
Predicted improvement:        0.000036990
lambda =          1; f =         -741.5339872
Norm of dx 0.00066525
Done for param rhov =   0.1615; f = -741.5340
Predicted improvement:        0.000033044
lambda =          1; f =         -741.5340202
Norm of dx 0.00066633
Done for param rhog =   0.6442; f = -741.5340
Sequence of univariate steps!!
Actual dxnorm 0.0037704
FVAL          -741.534
Improvement   0.0016716
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  2.091790e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.21445 s.
 
Iteration 28
Near-singular H problem.
Correct for low angle: 2.00417e-21
Predicted improvement: 53873734027.308540344
lambda =          1; f = 227451332644996382720.0000000
lambda =    0.33333; f = 25272370293026639872.0000000
lambda =    0.11111; f = 2808041143382340608.0000000
lambda =   0.037037; f = 312004571391164224.0000000
lambda =   0.012346; f = 34667174567097012.0000000
lambda =  0.0041152; f = 3851908274592160.0000000
lambda =  0.0013717; f = 427989804740640.8125000
lambda = 0.00045725; f = 47554421566068.5156250
lambda = 0.00015242; f = 5283824223722.2373047
lambda = 5.0805e-05; f = 587091448404.3460693
lambda = 1.6935e-05; f =  65232338720.3298645
lambda =  5.645e-06; f =   7248022390.9374323
lambda = 1.8817e-06; f =    805330306.7901363
lambda = 6.2723e-07; f =     89478874.1815175
lambda = 2.0908e-07; f =      9940906.7356007
lambda = 6.9692e-08; f =      1103710.1994459
lambda = 2.3231e-08; f =       121917.2880044
lambda = 7.7435e-09; f =        12868.4515914
lambda = 2.5812e-09; f =          764.9669052

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =     14156550.3186127
lambda = -2.0908e-07; f =      1571952.3588265
lambda = -6.9692e-08; f =       173891.2414418
lambda = -2.3231e-08; f =        18626.9655591
lambda = -7.7435e-09; f =         1400.1400189
lambda = -2.5812e-09; f =         -506.6305912
Norm of dx 1.6231e+10
Predicted improvement:        0.227968448
lambda =          1; f =         -741.4906821
lambda =    0.33333; f =         -741.5340197
lambda =    0.11111; f =         -737.9731312
lambda =   0.037037; f =         -741.1495814
lambda =   0.012346; f =         -741.4950549
lambda =  0.0041152; f =         -741.5309415
lambda =  0.0013717; f =         -741.5340951
lambda = 0.00045725; f =         -741.5341675
lambda = 0.00088394; f =         -741.5341946
Norm of dx     1.4266
Predicted improvement:        0.000111010
lambda =          1; f =         -741.5343059
Norm of dx 9.1648e-05
Done for param e_a =   0.0721; f = -741.5343
Predicted improvement:        0.000014759
lambda =          1; f =         -741.5343206
Norm of dx 2.1748e-05
Done for param e_g =   0.0466; f = -741.5343
Predicted improvement:        0.000017729
lambda =          1; f =         -741.5343384
Norm of dx 1.8555e-05
Done for param e_rer =   0.0363; f = -741.5343
Predicted improvement:        0.000082728
lambda =          1; f =         -741.5344211
Norm of dx 8.3161e-05
Done for param alp =   0.3430; f = -741.5344
Predicted improvement:        0.000050221
lambda =          1; f =         -741.5344714
Norm of dx  3.827e-05
Done for param bet =   0.9359; f = -741.5345
Predicted improvement:        0.000171404
lambda =          1; f =         -741.5346428
Norm of dx 1.8499e-05
Done for param delt =   0.0999; f = -741.5346
Predicted improvement:        0.000050596
lambda =          1; f =         -741.5346934
Norm of dx 0.00069751
Done for param phi1 =   1.4159; f = -741.5347
Predicted improvement:        0.000053844
lambda =          1; f =         -741.5347472
Norm of dx  0.0038813
Done for param phi2 =   5.5968; f = -741.5347
Predicted improvement:        0.000336894
lambda =          1; f =         -741.5350835
Norm of dx 0.00056229
Done for param hf =   0.5512; f = -741.5351
Predicted improvement:        0.000024103
lambda =          1; f =         -741.5351076
Norm of dx 0.00053624
Done for param rhov =   0.1610; f = -741.5351
Predicted improvement:        0.000018116
lambda =          1; f =         -741.5351258
Norm of dx 0.00058483
Done for param rhorer =   0.5873; f = -741.5351
Sequence of univariate steps!!
Actual dxnorm 0.0032956
FVAL          -741.5351
Improvement   0.0011055
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  5.334431e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.26155 s.
 
Iteration 29
Near-singular H problem.
Correct for low angle: 3.56015e-21
Predicted improvement: 15915711031.583002090
lambda =          1; f = 28292294924679876608.0000000
lambda =    0.33333; f = 3143588324679906816.0000000
lambda =    0.11111; f = 349287591536258624.0000000
lambda =   0.037037; f = 38809732361303256.0000000
lambda =   0.012346; f = 4312192474050685.5000000
lambda =  0.0041152; f = 479132493603457.1250000
lambda =  0.0013717; f = 53236942562190.2812500
lambda = 0.00045725; f = 5915215449301.4492188
lambda = 0.00015242; f = 657246030286.6429443
lambda = 5.0805e-05; f =  73027292683.3504791
lambda = 1.6935e-05; f =   8114128526.8801489
lambda =  5.645e-06; f =    901564368.5708246
lambda = 1.8817e-06; f =    100171563.2655825
lambda = 6.2723e-07; f =     11128988.4886204
lambda = 2.0908e-07; f =      1235721.0037884
lambda = 6.9692e-08; f =       136585.7118844
lambda = 2.3231e-08; f =        14498.4519579
lambda = 7.7435e-09; f =          946.1542378
lambda = 2.5812e-09; f =         -555.5131525

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =      1460306.5654887
lambda = -2.0908e-07; f =       161507.6697434
lambda = -6.9692e-08; f =        17256.9393905
lambda = -2.3231e-08; f =         1249.1664834
lambda = -7.7435e-09; f =         -523.0358365
lambda = -2.5812e-09; f =         -717.9692275
Norm of dx 5.6575e+09
Predicted improvement:        0.059108342
lambda =          1; f =         -723.3919729
lambda =    0.33333; f =         -739.5598991
lambda =    0.11111; f =         -741.3246798
lambda =   0.037037; f =         -741.5146687
lambda =   0.012346; f =         -741.5338260
lambda =  0.0041152; f =         -741.5353057
Norm of dx    0.31545
Predicted improvement:        0.000067467
lambda =          1; f =         -741.5353732
Norm of dx 7.1518e-05
Done for param e_a =   0.0721; f = -741.5354
Predicted improvement:        0.000010511
lambda =          1; f =         -741.5353838
Norm of dx 1.8344e-05
Done for param e_g =   0.0466; f = -741.5354
Predicted improvement:        0.000012552
lambda =          1; f =         -741.5353963
Norm of dx 1.5604e-05
Done for param e_rer =   0.0363; f = -741.5354
Predicted improvement:        0.000085222
lambda =          1; f =         -741.5354816
Norm of dx 8.4453e-05
Done for param alp =   0.3432; f = -741.5355
Predicted improvement:        0.000015918
lambda =          1; f =         -741.5354975
Norm of dx 2.1575e-05
Done for param bet =   0.9357; f = -741.5355
Predicted improvement:        0.000154760
lambda =          1; f =         -741.5356522
Norm of dx 1.7578e-05
Done for param delt =   0.0999; f = -741.5357
Predicted improvement:        0.000019834
lambda =          1; f =         -741.5356721
Norm of dx 0.00038971
Done for param sig =   2.0790; f = -741.5357
Predicted improvement:        0.000039942
lambda =          1; f =         -741.5357120
Norm of dx  0.0033439
Done for param phi2 =   5.5991; f = -741.5357
Predicted improvement:        0.000167323
lambda =          1; f =         -741.5358791
Norm of dx  0.0003956
Done for param hf =   0.5516; f = -741.5359
Predicted improvement:        0.000014868
lambda =          1; f =         -741.5358940
Norm of dx 0.00044666
Done for param rhog =   0.6446; f = -741.5359
Sequence of univariate steps!!
Actual dxnorm 0.0025539
FVAL          -741.5359
Improvement   0.00076825
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  3.881876e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.21377 s.
 
Iteration 30
Near-singular H problem.
Correct for low angle: 2.72855e-21
Predicted improvement: 19818832575.634162903
lambda =          1; f = 63042520697552019456.0000000
lambda =    0.33333; f = 7004724521513384960.0000000
lambda =    0.11111; f = 778302724466985216.0000000
lambda =   0.037037; f = 86478080447793568.0000000
lambda =   0.012346; f = 9608675589130566.0000000
lambda =  0.0041152; f = 1067630615620800.3750000
lambda =  0.0013717; f = 118625622159531.7812500
lambda = 0.00045725; f = 13180624084514.8339844
lambda = 0.00015242; f = 1464513586776.5058594
lambda = 5.0805e-05; f = 162723664634.0442810
lambda = 1.6935e-05; f =  18080384338.6999741
lambda =  5.645e-06; f =   2008923546.0439734
lambda = 1.8817e-06; f =    223210612.0985045
lambda = 6.2723e-07; f =     24799707.8507407
lambda = 2.0908e-07; f =      2754595.4811885
lambda = 6.9692e-08; f =       305318.1109358
lambda = 2.3231e-08; f =        33236.0336432
lambda = 7.7435e-09; f =         3024.6425254
lambda = 2.5812e-09; f =         -325.6427602

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =      2826549.9911110
lambda = -2.0908e-07; f =       313293.3948797
lambda = -6.9692e-08; f =        34116.8598943
lambda = -2.3231e-08; f =         3120.8102055
lambda = -7.7435e-09; f =         -315.8047683
lambda = -2.5812e-09; f =         -695.1878893
Norm of dx 8.3804e+09
Predicted improvement:        0.041949702
lambda =          1; f =         -726.2311617
lambda =    0.33333; f =         -739.8552160
lambda =    0.11111; f =         -741.3553937
lambda =   0.037037; f =         -741.5179108
lambda =   0.012346; f =         -741.5345864
lambda =  0.0041152; f =         -741.5359789
lambda =  0.0013717; f =         -741.5359802
lambda =  0.0026518; f =         -741.5360084
Norm of dx    0.22828
Predicted improvement:        0.000043754
lambda =          1; f =         -741.5360522
Norm of dx 5.7657e-05
Done for param e_a =   0.0721; f = -741.5361
Predicted improvement:        0.000011370
lambda =          1; f =         -741.5360636
Norm of dx 1.4845e-05
Done for param e_rer =   0.0363; f = -741.5361
Predicted improvement:        0.000048067
lambda =          1; f =         -741.5361116
Norm of dx 6.3458e-05
Done for param alp =   0.3433; f = -741.5361
Predicted improvement:        0.000057935
lambda =          1; f =         -741.5361696
Norm of dx 1.0755e-05
Done for param delt =   0.0999; f = -741.5362
Predicted improvement:        0.000012689
lambda =          1; f =         -741.5361823
Norm of dx 0.00031146
Done for param sig =   2.0782; f = -741.5362
Predicted improvement:        0.000065870
lambda =          1; f =         -741.5362481
Norm of dx 0.00024786
Done for param hf =   0.5519; f = -741.5362
Predicted improvement:        0.000015827
lambda =          1; f =         -741.5362639
Norm of dx 0.00043403
Done for param rhov =   0.1605; f = -741.5363
Sequence of univariate steps!!
Actual dxnorm 0.0010207
FVAL          -741.5363
Improvement   0.0003699
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  3.362408e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.23536 s.
 
Iteration 31
Correct for low angle: 9.49981e-13
Predicted improvement: 290923731.148052454
lambda =          1; f = 20907076016682292.0000000
lambda =    0.33333; f = 2323008438700179.0000000
lambda =    0.11111; f = 258112046211408.2187500
lambda =   0.037037; f = 28679115400921.0937500
lambda =   0.012346; f = 3186568095844.2783203
lambda =  0.0041152; f = 354063027309.6617432
lambda =  0.0013717; f =  39340304445.2697067
lambda = 0.00045725; f =   4371133858.6738768
lambda = 0.00015242; f =    485677407.7569147
lambda = 5.0805e-05; f =     53962340.1862585
lambda = 1.6935e-05; f =      5994771.2999284
lambda =  5.645e-06; f =       665298.7419542
lambda = 1.8817e-06; f =        73220.8971630
lambda = 6.2723e-07; f =         7463.0608845
lambda = 2.0908e-07; f =          166.1433495
lambda = 6.9692e-08; f =         -641.7917772
lambda = 2.3231e-08; f =         -730.7755808
lambda = 7.7435e-09; f =         -740.4234651
lambda = 2.5812e-09; f =         -741.4334371

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =    341987611.8362156
lambda = -2.0908e-07; f =     37971825.4546936
lambda = -6.9692e-08; f =      4209725.7869789
lambda = -2.3231e-08; f =       464191.9163312
lambda = -7.7435e-09; f =        49958.3176501
lambda = -2.5812e-09; f =         4577.9727047
Norm of dx 2.9489e+10
Predicted improvement:        0.000952503
lambda =          1; f =         -741.2052982
lambda =    0.33333; f =         -741.4999127
lambda =    0.11111; f =         -741.5323660
lambda =   0.037037; f =         -741.5358778
lambda =   0.012346; f =         -741.5362367
lambda =  0.0041152; f =         -741.5362661
lambda =  0.0013717; f =         -741.5362659
lambda =  0.0026518; f =         -741.5362666
Norm of dx   0.019369
Predicted improvement:        0.000010608
lambda =          1; f =         -741.5362772
Norm of dx  2.843e-05
Done for param e_a =   0.0721; f = -741.5363
Predicted improvement:        0.000017072
lambda =          1; f =         -741.5362943
Norm of dx 0.00011632
Done for param e_v =   0.2323; f = -741.5363
Predicted improvement:        0.000019624
lambda =          1; f =         -741.5363139
Norm of dx 2.5061e-05
Done for param e_g =   0.0466; f = -741.5363
Predicted improvement:        0.000021749
lambda =          1; f =         -741.5363357
Norm of dx 4.2696e-05
Done for param alp =   0.3433; f = -741.5363
Predicted improvement:        0.000019728
lambda =          1; f =         -741.5363554
Norm of dx 0.00043538
Done for param phi1 =   1.4170; f = -741.5364
Predicted improvement:        0.000010148
lambda =          1; f =         -741.5363655
Norm of dx  0.0016861
Done for param phi2 =   5.6011; f = -741.5364
Predicted improvement:        0.000044956
lambda =          1; f =         -741.5364105
Norm of dx 0.00020463
Done for param hf =   0.5522; f = -741.5364
Predicted improvement:        0.000011421
lambda =          1; f =         -741.5364219
Norm of dx 0.00036826
Done for param rhov =   0.1602; f = -741.5364
Sequence of univariate steps!!
Actual dxnorm 0.0017972
FVAL          -741.5364
Improvement   0.00015799
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  2.180600e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.23799 s.
 
Iteration 32
Correct for low angle: 2.55127e-11
Predicted improvement: 20401746.513143253
lambda =          1; f = 3597778908227828224.0000000
lambda =    0.33333; f = 399753209343646848.0000000
lambda =    0.11111; f = 44417022366515592.0000000
lambda =   0.037037; f = 4935224409426999.0000000
lambda =   0.012346; f = 548358168392476.8750000
lambda =  0.0041152; f = 60928652269303.5312500
lambda =  0.0013717; f = 6769839215838.5292969
lambda = 0.00045725; f = 752200678116.3610840
lambda = 0.00015242; f =  83576626291.5184174
lambda = 5.0805e-05; f =   9285882433.1237469
lambda = 1.6935e-05; f =   1031627822.9054940
lambda =  5.645e-06; f =    114579250.1564419
lambda = 1.8817e-06; f =     12715240.2244345
lambda = 6.2723e-07; f =      1407108.5472083
lambda = 2.0908e-07; f =       154013.3339259
lambda = 6.9692e-08; f =        15901.8143167
lambda = 2.3231e-08; f =          929.8435866
lambda = 7.7435e-09; f =         -609.1247737
lambda = 2.5812e-09; f =         -738.5944365

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =         -732.9756708
lambda = -2.0908e-07; f =         -740.6171777
lambda = -6.9692e-08; f =         -741.4419259
lambda = -2.3231e-08; f =         -741.5268511
lambda = -7.7435e-09; f =         -741.5356115
lambda = -2.5812e-09; f =         -741.5363933
Norm of dx 1.8968e+09
Predicted improvement:        0.000914266
lambda =          1; f =         -741.5164409
lambda =    0.33333; f =         -741.5346070
lambda =    0.11111; f =         -741.5363557
lambda =   0.037037; f =         -741.5364597
Norm of dx   0.094582
Predicted improvement:        0.000011991
lambda =          1; f =         -741.5364717
Norm of dx 1.5239e-05
Done for param e_rer =   0.0363; f = -741.5365
Predicted improvement:        0.000014582
lambda =          1; f =         -741.5364863
Norm of dx 3.4963e-05
Done for param alp =   0.3432; f = -741.5365
Predicted improvement:        0.000015725
lambda =          1; f =         -741.5365020
Norm of dx 2.1467e-05
Done for param bet =   0.9356; f = -741.5365
Predicted improvement:        0.000014753
lambda =          1; f =         -741.5365167
Norm of dx 0.00011708
Done for param hf =   0.5525; f = -741.5365
Predicted improvement:        0.000011068
lambda =          1; f =         -741.5365278
Norm of dx 0.00038521
Done for param rhog =   0.6450; f = -741.5365
Sequence of univariate steps!!
Actual dxnorm 0.0035327
FVAL          -741.5365
Improvement   0.00010591
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.734493e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.25387 s.
 
Iteration 33
Near-singular H problem.
Correct for low angle: 2.63263e-21
Predicted improvement: 43027576069.150833130
lambda =          1; f = 284291518055925055488.0000000
lambda =    0.33333; f = 31587946449974104064.0000000
lambda =    0.11111; f = 3509771827546818560.0000000
lambda =   0.037037; f = 389974647429174784.0000000
lambda =   0.012346; f = 43330516355676536.0000000
lambda =  0.0041152; f = 4814501808849330.0000000
lambda =  0.0013717; f = 534944642611250.8750000
lambda = 0.00045725; f = 59438292684222.1171875
lambda = 0.00015242; f = 6604254429175.3466797
lambda = 5.0805e-05; f = 733805942748.2075195
lambda = 1.6935e-05; f =  81533958226.5700226
lambda =  5.645e-06; f =   9059316454.9507809
lambda = 1.8817e-06; f =   1006586205.4028722
lambda = 6.2723e-07; f =    111840974.9230331
lambda = 2.0908e-07; f =     12425696.5448791
lambda = 6.9692e-08; f =      1379840.5654175
lambda = 2.3231e-08; f =       152613.8269779
lambda = 7.7435e-09; f =        16284.2987446
lambda = 2.5812e-09; f =         1146.2288762

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =     57844428.5174861
lambda = -2.0908e-07; f =      6426205.4957272
lambda = -6.9692e-08; f =       713267.5311002
lambda = -2.3231e-08; f =        78562.6099096
lambda = -7.7435e-09; f =         8061.6822670
lambda = -2.5812e-09; f =          234.4414965
Norm of dx 2.0768e+10
Predicted improvement:        0.000314040
lambda =          1; f =         -741.5365103
lambda =    0.33333; f =         -741.5366654
Norm of dx 0.00075555
Predicted improvement:        0.000020754
lambda =          1; f =         -741.5366862
Norm of dx 2.5757e-05
Done for param e_g =   0.0466; f = -741.5367
Predicted improvement:        0.000026034
lambda =          1; f =         -741.5367122
Norm of dx 0.00044567
Done for param sig =   2.0777; f = -741.5367
Predicted improvement:        0.000019134
lambda =          1; f =         -741.5367313
Norm of dx 0.00060072
Done for param rhorer =   0.5879; f = -741.5367
Sequence of univariate steps!!
Actual dxnorm 0.00078967
FVAL          -741.5367
Improvement   0.00020351
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.159602e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.2281 s.
 
Iteration 34
Near-singular H problem.
Correct for low angle: 3.55349e-21
Predicted improvement: 15957411556.116754532
lambda =          1; f = 39060029557372846080.0000000
lambda =    0.33333; f = 4340003283980069376.0000000
lambda =    0.11111; f = 482222587051407168.0000000
lambda =   0.037037; f = 53580287430992760.0000000
lambda =   0.012346; f = 5953365263722009.0000000
lambda =  0.0041152; f = 661485027172582.5000000
lambda =  0.0013717; f = 73498335642119.7968750
lambda = 0.00045725; f = 8166481500778.1259766
lambda = 0.00015242; f = 907386753902.5341797
lambda = 5.0805e-05; f = 100820723488.9983215
lambda = 1.6935e-05; f =  11202293189.4859200
lambda =  5.645e-06; f =   1244695664.2780249
lambda = 1.8817e-06; f =    138297886.3897906
lambda = 6.2723e-07; f =     15365449.0027198
lambda = 2.0908e-07; f =      1706505.6413636
lambda = 6.9692e-08; f =       188917.3839857
lambda = 2.3231e-08; f =        20320.5052013
lambda = 7.7435e-09; f =         1595.3128628
lambda = 2.5812e-09; f =         -482.8715791

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =      8171118.3769692
lambda = -2.0908e-07; f =       907151.9770253
lambda = -6.9692e-08; f =       100105.8418480
lambda = -2.3231e-08; f =        10454.4507636
lambda = -7.7435e-09; f =          499.7104023
lambda = -2.5812e-09; f =         -604.3394410
Norm of dx 7.7352e+09
Predicted improvement:        0.000001166
lambda =          1; f =         -741.5367327
Norm of dx 6.7003e-05
Predicted improvement:        0.000013391
lambda =          1; f =         -741.5367461
Norm of dx 0.00010299
Done for param e_v =   0.2323; f = -741.5367
Predicted improvement:        0.000020503
lambda =          1; f =         -741.5367666
Norm of dx 1.9915e-05
Done for param e_rer =   0.0362; f = -741.5368
Predicted improvement:        0.000010978
lambda =          1; f =         -741.5367776
Norm of dx 1.7949e-05
Done for param bet =   0.9355; f = -741.5368
Sequence of univariate steps!!
Actual dxnorm 0.00012576
FVAL          -741.5368
Improvement   4.6262e-05
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  9.182837e-26.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.20286 s.
 
Iteration 35
Correct for low angle: 4.92668e-12
Predicted improvement: 78386266.000806168
lambda =          1; f = 1001332936654789.0000000
lambda =    0.33333; f = 111259214446720.6875000
lambda =    0.11111; f = 12362134692370.7617188
lambda =   0.037037; f = 1373570438884.4111328
lambda =   0.012346; f = 152618909718.2543335
lambda =  0.0041152; f =  16957646884.6419182
lambda =  0.0013717; f =   1884179298.0792506
lambda = 0.00045725; f =    209351586.7777846
lambda = 0.00015242; f =     23260292.3683427
lambda = 5.0805e-05; f =      2583706.3827192
lambda = 1.6935e-05; f =       286382.7746437
lambda =  5.645e-06; f =        31149.5408894
lambda = 1.8817e-06; f =         2798.6011669
lambda = 6.2723e-07; f =         -349.0453403
lambda = 2.0908e-07; f =         -698.1633493
lambda = 6.9692e-08; f =         -736.7924171
lambda = 2.3231e-08; f =         -741.0326045
lambda = 7.7435e-09; f =         -741.4865091
lambda = 2.5812e-09; f =         -741.5322447

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =     26475943.2411469
lambda = -2.0908e-07; f =      2933842.7954068
lambda = -6.9692e-08; f =       322906.2214648
lambda = -2.3231e-08; f =        34419.6047936
lambda = -7.7435e-09; f =         2904.6784268
lambda = -2.5812e-09; f =         -417.2661060
Norm of dx 8.2088e+09
Predicted improvement:        0.000008900
lambda =          1; f =         -741.5367863
Norm of dx 2.7185e-05
Sequence of univariate steps!!
Try diagonal Hessian
Predicted improvement:        0.000144291
lambda =          1; f =         -741.5323689
lambda =    0.33333; f =         -741.5363632
lambda =    0.11111; f =         -741.5367619
lambda =   0.037037; f =         -741.5367911
Norm of dx 0.00067364
Actual dxnorm 3.7109e-05
FVAL          -741.5368
Improvement   1.3518e-05
Ftol          1e-05
Htol          1e-05
[Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  1.352327e-25.] 
[> In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('mr_hessian', 'C:\dynare\6.4\matlab\optimization\mr_hessian.m', 258)" style="font-weight:bold">mr_hessian</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\mr_hessian.m',258,0)">line 258</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('newrat', 'C:\dynare\6.4\matlab\optimization\newrat.m', 275)" style="font-weight:bold">newrat</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\newrat.m',275,0)">line 275</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_minimize_objective', 'C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m', 338)" style="font-weight:bold">dynare_minimize_objective</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\optimization\dynare_minimize_objective.m',338,0)">line 338</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation_1', 'C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m', 244)" style="font-weight:bold">dynare_estimation_1</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation_1.m',244,0)">line 244</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare_estimation', 'C:\dynare\6.4\matlab\estimation\dynare_estimation.m', 105)" style="font-weight:bold">dynare_estimation</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\estimation\dynare_estimation.m',105,0)">line 105</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('ecuador.driver', 'E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m', 609)" style="font-weight:bold">ecuador.driver</a> (<a href="matlab: opentoline('E:\My New Papers & Books\Paper_2 DSGE dollarized economies\DEF_FOLDER\Din_Mat_Estimation\+ecuador\driver.m',609,0)">line 609</a>)
In <a href="matlab:matlab.lang.internal.introspective.errorDocCallback('dynare', 'C:\dynare\6.4\matlab\dynare.m', 308)" style="font-weight:bold">dynare</a> (<a href="matlab: opentoline('C:\dynare\6.4\matlab\dynare.m',308,0)">line 308</a>)
] 
Elapsed time for iteration 0.19175 s.
 
Iteration 36
Near-singular H problem.
Correct for low angle: 5.60913e-21
Predicted improvement: 9724862631.972906113
lambda =          1; f = 40173606667000578048.0000000
lambda =    0.33333; f = 4463734073963246592.0000000
lambda =    0.11111; f = 495970452613273216.0000000
lambda =   0.037037; f = 55107828051704504.0000000
lambda =   0.012346; f = 6123092000265519.0000000
lambda =  0.0041152; f = 680343553758149.5000000
lambda =  0.0013717; f = 75593727585944.5156250
lambda = 0.00045725; f = 8399302861530.9677734
lambda = 0.00015242; f = 933255805206.5874023
lambda = 5.0805e-05; f = 103695066262.9197235
lambda = 1.6935e-05; f =  11521665855.5259476
lambda =  5.645e-06; f =   1280181931.6349339
lambda = 1.8817e-06; f =    142240943.5127711
lambda = 6.2723e-07; f =     15803612.6396253
lambda = 2.0908e-07; f =      1755205.8857146
lambda = 6.9692e-08; f =       194333.6570560
lambda = 2.3231e-08; f =        20924.0277970
lambda = 7.7435e-09; f =         1663.1158429
lambda = 2.5812e-09; f =         -475.0468806

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =       320507.6141100
lambda = -2.0908e-07; f =        34865.2503159
lambda = -6.9692e-08; f =         3188.0401628
lambda = -2.3231e-08; f =         -311.9042281
lambda = -7.7435e-09; f =         -695.5269318
lambda = -2.5812e-09; f =         -736.8344817
Norm of dx 6.4024e+09
Norm of dx          0
Sequence of univariate steps!!
Try diagonal Hessian
Correct for low angle: 7.2584e-12
Predicted improvement: 2810352544.573718071
lambda =          1; f = 136935695205759130271744.0000000
lambda =    0.33333; f = 15215077244561208115200.0000000
lambda =    0.11111; f = 1690564138110195335168.0000000
lambda =   0.037037; f = 187840459731894173696.0000000
lambda =   0.012346; f = 20871162173056839680.0000000
lambda =  0.0041152; f = 2319018012769916416.0000000
lambda =  0.0013717; f = 257668665932672896.0000000
lambda = 0.00045725; f = 28629851052672036.0000000
lambda = 0.00015242; f = 3181094322199259.0000000
lambda = 5.0805e-05; f = 353454844952124.4375000
lambda = 1.6935e-05; f = 39272733970909.2187500
lambda =  5.645e-06; f = 4363628247670.3247070
lambda = 1.8817e-06; f = 484844629238.7881470
lambda = 6.2723e-07; f =  53870640426.2131348
lambda = 2.0908e-07; f =   5985297932.9032335
lambda = 6.9692e-08; f =    664923077.0831059
lambda = 2.3231e-08; f =     73843233.4217423
lambda = 7.7435e-09; f =      8192000.9391434
lambda = 2.5812e-09; f =       905521.3381079

lambda =

  -6.2723e-07

lambda = -6.2723e-07; f =        26006.5292400
lambda = -2.0908e-07; f =         2207.7165628
lambda = -6.9692e-08; f =         -419.6634866
lambda = -2.3231e-08; f =         -707.2071155
lambda = -7.7435e-09; f =         -738.0685617
lambda = -2.5812e-09; f =         -741.2518914
Norm of dx 3.7005e+11
Try gradient direction
Predicted improvement:        0.000461430
lambda =          1; f =         -741.4772315
lambda =    0.33333; f =         -741.5305672
lambda =    0.11111; f =         -741.5361747
lambda =   0.037037; f =         -741.5367457
lambda =   0.012346; f =         -741.5367937
lambda =  0.0041152; f =         -741.5367939
lambda =  0.0079555; f =         -741.5367948
Norm of dx 0.00030379
No further improvement is possible!
Actual dxnorm 2.4168e-06
FVAL          -741.5368
Improvement   3.6752e-06
Ftol          1e-05
Htol          1e-05
Gradient norm  3.0379
Minimum Hessian eigenvalue -4.812e-13
Maximum Hessian eigenvalue 3981810.4893
Estimation successful.

Final value of minus the log posterior (or likelihood):-741.536795 

RESULTS FROM POSTERIOR ESTIMATION
parameters
       prior mean     mode    s.d.  prior pstdev

alp        0.3300   0.3434  0.0202   norm 0.0200 
bet        0.9450   0.9355  0.0134   unif 0.0260 
delt       0.1000   0.0999  0.0010   norm 0.0010 
sig        2.0000   2.0776  0.0948   norm 0.1000 
phi1       1.5000   1.4173  0.1011   norm 0.1000 
phi2       5.6000   5.6046  0.4934   norm 0.5000 
psi1       1.4000   1.4000  0.5000   norm 0.5000 
hf         0.5000   0.5525  0.0570   beta 0.2000 
rhoa       0.7000   0.3458  0.1001   beta 0.2000 
rhov       0.5000   0.1601  0.0960   beta 0.2000 
rhog       0.5000   0.6451  0.0869   beta 0.2000 
rhorer     0.0000   0.5879  0.1111   unif 0.5774 
rhoyw      0.5500   0.5552  0.0729   beta 0.1000 

standard deviation of shocks
       prior mean     mode    s.d.  prior pstdev

e_a        0.0100   0.0722  0.0113   invg    Inf 
e_v        0.0100   0.2323  0.0265   invg    Inf 
e_g        0.0100   0.0466  0.0048   invg    Inf 
e_rer      0.0100   0.0362  0.0060   invg    Inf 
e_yw       0.0100   0.0097  0.0008   invg    Inf 


Log data density [Laplace approximation] is 693.646680.

Estimation::mcmc: Multiple chains mode.
Estimation::mcmc: Searching for initial values...
Estimation::mcmc: Initial values found!

Estimation::mcmc: Write details about the MCMC... Ok!
Estimation::mcmc: Details about the MCMC are available in ecuador/metropolis\ecuador_mh_history_0.mat


Estimation::mcmc: Number of mh files: 1 per block.
Estimation::mcmc: Total number of generated files: 2.
Estimation::mcmc: Total number of iterations: 20000.
Estimation::mcmc: Current acceptance ratio per chain:
                                                       Chain  1: 29.8%
                                                       Chain  2: 29.515%
Estimation::mcmc: Total number of MH draws per chain: 20000.
Estimation::mcmc: Total number of generated MH files: 1.
Estimation::mcmc: I'll use mh-files 1 to 1.
Estimation::mcmc: In MH-file number 1 I'll start at line 10001.
Estimation::mcmc: Finally I keep 10000 draws per chain.

marginal density: I'm computing the posterior mean and covariance...  Done!
marginal density: I'm computing the posterior log marginal density (modified harmonic mean)... Done!


ESTIMATION RESULTS

Log data density (Modified Harmonic Mean) is 693.562174.
parameters
         prior mean   post. mean        90% HPD interval    prior       pstdev

alp           0.330       0.3446      0.3102      0.3757     norm       0.0200
bet           0.945       0.9383      0.9174      0.9609     unif       0.0260
delt          0.100       0.0998      0.0982      0.1014     norm       0.0010
sig           2.000       2.0875      1.9253      2.2422     norm       0.1000
phi1          1.500       1.4055      1.2263      1.5617     norm       0.1000
phi2          5.600       5.6342      4.8194      6.5045     norm       0.5000
psi1          1.400       1.4096      0.6126      2.2449     norm       0.5000
hf            0.500       0.5523      0.4611      0.6595     beta       0.2000
rhoa          0.700       0.3551      0.1708      0.5090     beta       0.2000
rhov          0.500       0.2034      0.0425      0.3507     beta       0.2000
rhog          0.500       0.6268      0.4854      0.7785     beta       0.2000
rhorer        0.000       0.5596      0.3640      0.7460     unif       0.5774
rhoyw         0.550       0.5485      0.4328      0.6713     beta       0.1000

standard deviation of shocks
         prior mean   post. mean        90% HPD interval    prior       pstdev

e_a           0.010       0.0770      0.0551      0.0971     invg          Inf
e_v           0.010       0.2563      0.2034      0.3081     invg          Inf
e_g           0.010       0.0497      0.0413      0.0587     invg          Inf
e_rer         0.010       0.0413      0.0305      0.0534     invg          Inf
e_yw          0.010       0.0100      0.0084      0.0113     invg          Inf
Estimation::mcmc: Posterior (dsge) IRFs...
Estimation::mcmc: Posterior IRFs, done!
Estimation::compute_moments_varendo: I'm computing endogenous moments (this may take a while)... 


Posterior mean variance decomposition (in percent)
          e_a     e_v     e_g   e_rer    e_yw
y       68.17    9.68   20.78    1.29    0.08
x       48.69   50.21    1.03    0.06    0.00
c       64.80   31.18    3.80    0.21    0.01
w       97.42    1.93    0.61    0.04    0.00
R        2.24    0.48   91.34    5.64    0.29
k       50.35   47.79    1.75    0.10    0.01
d        1.87    0.26   91.77    5.77    0.33
r       42.28   42.55   14.26    0.87    0.05
l       92.08    1.69    5.85    0.36    0.02
la      47.64   50.96    1.31    0.08    0.00
tb      21.25    2.98    6.35   65.50    3.92
a      100.00    0.00   -0.00    0.00    0.00
v        0.00  100.00    0.00    0.00    0.00
g        0.00    0.00  100.00    0.00    0.00
rer      0.00    0.00    0.00  100.00    0.00
yw       0.00    0.00    0.00    0.00  100.00


Done!

Estimation::mcmc: Smoothed variables
Estimation::mcmc: Smoothed variables, done!
Estimation::mcmc: Smoothed shocks
Estimation::mcmc: Smoothed shocks, done!
Estimation::mcmc: Trend_coefficients
Estimation::mcmc: Trend_coefficients, done!
Estimation::mcmc: Smoothed constant
Estimation::mcmc: Smoothed constant, done!
Estimation::mcmc: Smoothed trend
Estimation::mcmc: Smoothed trend, done!
Estimation::mcmc: Updated Variables
Estimation::mcmc: Updated Variables, done!
Total computing time : 0h06m37s
Note: warning(s) encountered in MATLAB/Octave code
